Evaluation of reanalysis and satellitebased precipitation datasets in driving
hydrological models in a humid region of
Southern China
Hongliang Xu, Chong-Yu Xu, Nils Roar
Sælthun, Bin Zhou & Youpeng Xu
Stochastic Environmental Research
and Risk Assessment
ISSN 1436-3240
Volume 29
Number 8
Stoch Environ Res Risk Assess (2015)
29:2003-2020
DOI 10.1007/s00477-014-1007-z
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Stoch Environ Res Risk Assess (2015) 29:2003–2020
DOI 10.1007/s00477-014-1007-z
ORIGINAL PAPER
Evaluation of reanalysis and satellite-based precipitation
datasets in driving hydrological models in a humid
region of Southern China
Hongliang Xu • Chong-Yu Xu • Nils Roar Sælthun
Bin Zhou • Youpeng Xu
•
Published online: 31 December 2014
Ó Springer-Verlag Berlin Heidelberg 2014
Abstract This paper compares the original and bias corrected global gridded precipitation datasets, tropical rainfall
measuring mission (TRMM) and water and global change
forcing data (WFD) with gauged precipitation and evaluates
the usefulness of gridded precipitation datasets for hydrological simulations using the distributed soil and water
assessment tool (SWAT) and lumped Xinanjiang model in
Xiangjiang River basin, Southern China. The results show
that the differences in areal mean rainfalls of original
TRMM and WFD datasets and gauged dataset are in
acceptable limits of less than 10 %, while larger differences
exist in maximal 5-day rainfalls, dry spells and Fréchet
distance. The bias correction methods are able to significantly improve the biases in the mean values of TRMM and
WFD datasets. The nonlinear bias correction method gives
good results in correcting the standard deviations of TRMM/
WFD data. The hydrological modelling results show that the
WFD datasets perform relatively better than TRMM datasets
even though the results are poor as compared with using
gauged rainfall as input in daily step hydrological models.
At monthly time step, both TRMM and WFD data produce
acceptable model simulation results in terms of Nash–
Sutcliffe efficiency (Ens [ 0.7 for original TRMM/WFD
data and Ens [ 0.8 for linearly corrected TRMM/WFD data)
and relative error (|RE| \ 10 %).
H. Xu C.-Y. Xu (&) N. R. Sælthun
Department of Geosciences, University of Oslo, PO Box 1047,
Blindern, 0316 Oslo, Norway
e-mail: c.y.xu@geo.uio.no
1 Introduction
H. Xu
e-mail: xuhongliang.nju@gmail.com
H. Xu Y. Xu
Department of Land Resources and Tourism, Nanjing
University, 22 Hankou Road Nanjing, Nanjing 210093, Jiangsu,
People’s Republic of China
C.-Y. Xu
Department of Hydrology and Water Resources, Wuhan
University, Wuhan, People’s Republic of China
B. Zhou
Department of Chemistry, University of Oslo, Oslo, Norway
B. Zhou
Tianjin Academy of Environmental Sciences, 17 Fukang Road,
Tianjin, People’s Republic of China
Keywords Tropical rainfall measuring mission (TRMM)
3B42 Water and global change forcing data (WFD) Xinanjiang model Soil and water assessment tool
(SWAT)
Information about precipitation rates, amounts and distribution is indispensable for a wide range of applications
including agronomy, hydrology, meteorology and climatology (Scheel et al. 2011). The spatial and temporal distribution of precipitation greatly influences land surface
hydrological fluxes and states (Gottschalck et al. 2005).
The precise measurement of precipitation at fine space and
time resolution has been shown to improve our ability in
simulating land surface hydrological processes and states
(Faurès et al. 1995; Nykanen et al. 2001). Rain gauges
provide rainfall measurements at individual points, but
there is still a challenge in using gauged data to estimate
rainfall at basin level with appropriate spatial and temporal
scales (Sawunyama and Hughes 2008).
With technological advancements, high resolution global topographical and hydrological data are currently
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available. There are many global rainfall databases (e.g. the
Global Precipitation Climatology Project (GPCP), the
Global Precipitation Climatology Centre (GPCC), the
Climatic Research Unit (CRU) precipitation database, the
Climate Prediction Centre of NOAA (National Oceanic
and Atmospheric Administration) Merged Analysis of
Precipitation (CMAP), etc.), radar observations, and
numerical weather precipitation models. However, their
usefulness in driving hydrological models at basin scale is
yet to be evaluated (Cheema and Bastiaanssen 2012; Parkes et al. 2013; Adjei et al. 2014).
Much effort has been made in the correction of satellite
products by using gauged rainfall (Kang and Merwade
2014). The mismatch between the rainfall estimates from
satellite sensors and the gauges unavoidably leads to distrust in both datasets (Ciach et al. 2000; Grimes et al. 1999;
Omotosho and Oluwafemi 2009; Nair et al. 2009). Some
studies have compared the differences between the various
precipitation datasets and rain gauge data as well as their
impact on hydrological modeling (e.g. Abdella and Alfredsen 2010; Pan et al. 2010; Cheema and Bastiaanssen
2012; Fekete et al. 2004; Zhang et al. 2012; Li et al. 2013).
They have shown that global precipitation products can be
used as a source of information but need calibration and
validation due to the indirect nature of the radiation measurements. Comparing with gauged data, although these
global precipitation datasets generally agree in their longterm temporal trends and large scale spatial pattern,
remarkable differences at small time scale and at smaller
spatial scale (basin, grid, etc.) can be observed (Adler et al.
2001; Costa and Foley 1998; Oki et al. 1999; Castro et al.
2014). Fekete et al. (2004) compared six monthly precipitation datasets to assess their uncertainties and associated
impacts on the terrestrial water balance. The study demonstrated a need to improve precipitation estimates in arid
and semi-arid areas, where slight changes in rainfall could
result in significant changes in the runoff simulation due to
the nonlinearity of the runoff-generation processes. Biemans et al. (2009) compared seven global gridded precipitation datasets at river basin scale in terms of annual mean
and seasonal precipitation, which revealed that the representation of seasonality is similar for all the datasets but
noted large uncertainties in the mean annual precipitation,
especially in mountainous, arctic and small basins. Li et al.
(2013) compared two global gridded precipitation datasets
(Tropical rainfall measuring mission (TRMM) 3B42
(TRMM dataset contains a number of sub-datasets and
3B42 denotes one globally daily precipitation sub-dataset)
and WATCH (WATer and global CHange) forcing data
(WFD) and evaluated their performance in hydrological
simulations of the WASMOD-D model over 22 basins in
Southern Africa. The study showed that the two datasets
had similar spatial variation patterns in the mean annual
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rainfall and temporal trends, while WFD data slightly
outperformed the TRMM 3B42 data in the hydrological
simulations over Southern Africa. Li et al. (2012) studied
the difference of TRMM 3B42 rainfall with gauged precipitation data at different time scales and evaluated the
usefulness of the TRMM 3B42 rainfall for hydrological
processes simulation and water balance analysis using a
distributed water flow model for lake catchment (WATLAC) in Xinjiang catchment in China. The study suggested
that the TRMM 3B42 rainfall data were unsuitable for
daily streamflow simulations but better performance can be
achieved in monthly streamflow simulations. Rasmussen
et al. (2013) investigated the range of the TRMM rainfall
biases in storms over South America and indicated that the
TRMM rainfall data significantly underestimate the storms
with deep convective cores, while the bias is unrelated to
their echo top height. Müller and Thompson (2013)
adjusted the bias of the distributions of daily rainfall
observed by the TRMM satellite through stochastic modeling in Nepal with a sparse gauge network and complex
topography, and the results indicated that the bias adjusted
satellite data through stochastic modeling outperformed the
directly interpolated gauged rainfall. The study of Castro
et al. (2014) showed that the tropical rainfall measuring
mission multi-satellite precipitation analysis (TMPA) products for the rainfall are able to capture the mean spatial
pattern for flat areas on a monthly basis; however, satellite
estimates tend to miss precipitation that is enhanced by
flow lifting over the mountains. Although some studies
comparing global gridded precipitation datasets and their
performance in driving hydrological models have been
carried out, an integrated approach that (1) compares the
differences of original and bias corrected gridded precipitation datasets with gauged precipitation data in terms of
basic statistics, seasonal patterns as well as the spatial
distributions, and (2) examines the performances of the
original and bias corrected gridded precipitation datasets in
driving both distributed and lumped hydrological models at
different time and spatial resolutions has not been seen in
the literature, which motivated the current study.
This paper is therefore designed to: (1) compare and
evaluate the temporal characteristic and the spatial distribution of the original and bias corrected TRMM 3B42 and
WFD datasets with gauged precipitation data in a humid
climate basin in southern China, and (2) evaluate the performance of the original and bias corrected TRMM 3B42
and WFD data and gauged precipitation in driving a distributed hydrological model (SWAT) and a lumped
hydrological model (Xinanjiang model) in the simulation
of daily and monthly streamflows in the catchment. This
study contributes to the enhancement of understanding the
difference between the TRMM/WFD and gauged rainfall
and improves the knowledge regarding the utility of the
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raw and bias corrected TRMM 3B42 and WFD rainfall
datasets in driving lumped and distributed hydrological
models in varying time steps. The study significantly
contributes to hydrology as there is a clear need for evaluating the skills and usefulness of new global precipitation
datasets with different spatial and temporal resolutions as
an alternative data source for hydrological studies especially in data scarce regions.
2005
runoff of Xiangjiang River basin is 910 mm. The mean
annual temperature is about 17 °C, the annual mean relative humidity is about 75 % in summer and about 80 % in
winter, and the mean annual potential evapotranspiration is
about 1,000 mm. There are clear differences in runoff
among different seasons with 50 % of the flooding events
occurring in June and July.
2.2 Data
2 Study area and data
2.1 Study area
The Xiangjiang River basin (Fig. 1) is located between
24°300 -29°300 N and 110°300 -114°E in the central-south
China with a total area of 94,660 km2 and a total river
length of 856 km. The basin is surrounded by mountains in
its headwaters with plains in the downstream. The basin’s
rolling terrain accelerates the rainfall convergence and the
variation of runoff. It is heavily influenced by monsoon
climate, which brings heavy rainfall from the south in
summer. The mean annual precipitation reaches approximately 1,600 mm, of which 68 % occurs in the main rainy
season from April to September. The mean annual depth of
Three precipitation datasets were used in this study: (1)
TRMM 3B42 dataset, which is a number of climate rainfall
products produced from the passive microwave (TMI) and
precipitation radar (PR) sensors on board the tropical
rainfall measuring mission (TRMM) satellite launched in
November of 1997. The TRMM project aims to provide
small-scale variability of precipitation by frequent and
closely spaced datasets. TRMM uses a combination of
microwave and infrared sensors to improve accuracy,
coverage and resolution of its precipitation estimates;
however, the ability of TRMM to specify moderate and
light precipitation over short time intervals is poor (Huffman et al. 2007; Li et al. 2013). The TRMM 3B42 dataset,
covering from 1st January 1997 to 31st December 2008 in
time and 50°S–50°N in space, is one of the TRMM rainfall
Fig. 1 The Xiangjiang River basin in Southern China and the hydro-meteorological stations
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products which uses the TMI 2A12 rain estimates to adjust
high temporal resolution (3-hourly or higher) IR rain rates
over daily gridded 0.25 9 0.25° latitude/longitude boxes.
It has been used to drive distributed hydrological models in
large river basins as well as in flood modelling (Huffman
et al. 2007, 2010) (in the following text, TRMM 3B42 is
abbreviated as TRMM). (2) WFD dataset was developed
by the WATCH project (Weedon et al. 2011) as input for
large-scale land-surface and hydrological models. WFD
dataset is named as reanalysis data in the literature which is
weather observation data assimilated into global grids by a
numerical atmospheric model. The WFD dataset, from 1st
January 1958 to 31st December 2001, consists of meteorological variables needed to run hydrological models,
including 2 m air temperature, dew point temperature and
wind speed, 10 m air pressure and specific humidity,
downward long wave and shortwave radiation, rainfall and
snowfall rates (Uppala et al. 2005; Li et al. 2012). (3) The
gauged precipitation dataset which consists of 181 rain
gauges (Fig. 1) from 1st January 1963 to 31st December
2005 was provided by the Hydrology and Water Resources
Bureau of Hunan Province, China.
The meteorological data including daily maximum and
minimum air temperature, precipitation, wind speed, solar
radiation, average daily humidity and runoff used in this
study have been quality controlled by the China Meteorological Data Sharing Service System (http://cdc.cma.gov.
cn).
The spatial data for the watershed (i.e., elevation, land
use map, and soils distribution map) were prepared as
inputs to the SWAT Model. The DEM with 90 m resolution was downloaded from SRTM (The Shuttle Radar
Topography Mission; http://www2.jpl.nasa.gov/srtm/). The
1 km resolution soil dataset with respect to texture, depth
and drainage attributes was supplied by the SinoTropia
Project (Watershed EUTROphication management in
China through system oriented process modeling of Pressures, Impacts and Abatement actions). The land use
images which consist of five classes (forest, agriculture,
grass, urban, and water) were interpreted from the Landsat
satellite images (downed from http://www.landsat.org/) by
using ENVI Image Analysis Software.
Stoch Environ Res Risk Assess (2015) 29:2003–2020
5-day rainfall amount, maximum dry spell length, Nash–
Sutcliffe model efficiency coefficient (Ens) and Root mean
squared error (RMSE)) are assessed in the study. Meanwhile, the discrete Fréchet distance (Frechet 1906; Alt and
Godau 1995) which takes into account the location and
ordering of the points along the curves are used to measure
the closeness of two time series (TRMM/WFD and gauged
rainfall series) if stretching and compression in time is
allowed, but temporal succession is to be preserved. Unlike
similarity measures (e.g. RMSE) which average over a set
of similarity values, and dynamic time warping which
minimizes the sum of distances along the curves (data
series) (Driemel and Har-Peled 2013), the Fréchet distance
captures similarity under small non-affine distortions and
for some of its variants also spatiotemporal similarity
(Maheshwari et al. 2011).
Statistical methods used in this paper to evaluate the
TRMM/WFD and gauged rainfall datasets include: (1)
Kolmogorov–Smirnov (K–S) test for checking whether the
three datasets have the same distribution pattern; (2) student’s t test and (3) F test for checking the equality of mean
value and variance between the three datasets, respectively.
A 5 % significance level was used for the tests (1)–(3).
Meanwhile, the areal mean precipitation used in the analysis is computed by using Thiessen Polygons algorithm
(Zquiza 1998) and the spatial patterns of the three datasets
are interpolated on the catchment using Ordinary Kriging
method (Cressie 1988).
3.2 Bias correction methods
Due to the fact that satellite data and reanalysis data have
biases and random errors which are caused by various
factors like sampling frequency, nonuniform field-of-view
of the sensors, and uncertainties in the rainfall retrieval
algorithms, many algorithms have been developed for bias
correction (e.g. Yang et al. 1999; Li et al. 2010; Piani et al.
2010; Dosio and Paruolo 2011; Kang and Merwade 2014).
In this paper, two widely used bias correction algorithms
were adopted to correct the biases of TRMM and WFD
precipitation data.
3.2.1 Linear bias correction method
3 Methodology
3.1 Statistical analysis methods
In order to evaluate and compare the consistency and differences between the TRMM/WFD and gauged rainfall
datasets, several basic statistical indices (i.e. annual areal
mean rainfall, annual areal rainfall in wet/dry season, relative error (RE), Standard deviation (STDE), maximum
123
The linear bias correction method (Teutschbein and Seibert
2012) is simple and widely used, in which TRMM/WFD
daily precipitation amounts, P, are transformed into P*
such as P* = aP, using a scaling parameter, a ¼ O=P,
where O and P are monthly mean gauged and TRMM/
WFD precipitation respectively. The monthly scaling factor is applied to each uncorrected daily TRMM/WFD data
of that month to generate the corrected daily time series.
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2007
3.2.2 Nonlinear bias correction method
Leander and Buishand (2007) developed a nonlinear bias
correction algorithm to correct the precipitation P in the
following form:
P ¼ aPb
ð1Þ
where P* and P are the corrected and the original TRMM/
WFD precipitation data, respectively. The parameters a
and b are obtained for each grid point of the considered
domain on a monthly basis by using daily data iteratively
with a root finding algorithm (Brent 1973):
bðx; iÞ: CVðP
bðx;iÞ
Þ ¼ CVðPobs Þ
aðx; iÞ: aðx; iÞPbðx;iÞ ¼ Pobs
ð2Þ
ð3Þ
where x is the grid point to be corrected, i represents the
month, the overbar alludes to the monthly average, and
CV() is the coefficient of variation of the variables simulated at x in i by the TRMM/WFD (P) and of the observed
variables (Pobs).
of land management practices on the hydrology, sediment,
and contaminant transport in agricultural watersheds under
varying soils, land use, and management conditions (Arnold
et al. 1998). SWAT is based on the concept of Hydrologic
Response Units (HRUs), which are portions of a subbasin
that possess unique land use, management, and soil attributes. The runoff from each HRU is calculated separately
based on weather, soil properties, topography, and vegetation and land management and then summed to determine
the total loading from the subbasin. The SWAT Model is
based on the water balance equation (Neitsch et al. 2002):
SWt ¼ SW0 þ
t
X
ðRday Qsurf Ea Wseep Qgw Þ
ð4Þ
i¼1
where SWt is the final soil water content, SW0 is the initial
soil water content on day i, t is the time (days), Rday is the
amount of precipitation on day i, Qsurf is the amount of
surface runoff on day i, Ea is the amount of evapotranspiration on day i, Wseep is the amount of water entering the
vadose zone from the soil profile on day i, and Qgw is the
amount of return flow on day i.
3.3 Hydrological models
Xinanjiang Model is by far the most widely used hydrological model in China (Zhao et al. 1995) which was
developed in 1973 and the English version was first published in 1980 (Zhao et al. 1980). Its main feature is the
concept that runoff is not produced until the soil moisture
content of the aeration zone reaches field capacity, and
thereafter runoff equals the rainfall excess without further
loss. The inputs to the model are daily areal precipitation,
the measured daily pan evaporation and observed discharge
for calibration. The outputs are the modelled outlet discharge from the whole basin, the estimated actual evapotranspiration from the whole basin, which is the sum of the
evapotranspiration from the upper soil layer, the lower soil
layer, and the deepest layer (Zhao 1992). Xinanjiang model
has been applied successfully since it was published over
very large areas including all of the agricultural, pastoral
and forested lands of China except the loess. The model is
mainly used for hydrological forecasting (Yao et al. 2009),
and the use of the model has also spread to other fields of
application such as water resources estimation, design
flood and field drainage, water project programming,
hydrological impact of climate change and water quality
accounting, etc. (Hu et al. 2005; Ren et al. 2006; Liu et al.
2009; Yao et al. 2009; Bao et al. 2011; Zhang et al. 2012).
The US National Weather Service River Forecast System
has also reported its good performance in the arid Bird
Creek watershed in the United States (Singh 1995).
The SWAT (Soil and Water Assessment Tool) Model is a
distributed-parameter model designed to predict the effects
4 Results and discussion
The results of the study are presented in the following
order: (i) Comparison of original TRMM and WED data
with gauged precipitation data, (ii) comparison of bias
corrected TRMM and WED data with gauged precipitation
data, (iii) evaluation of daily and monthly model simulation results using original TRMM and WED data and
gauged precipitation data as inputs, and (iv) evaluation of
model simulation results using bias corrected TRMM and
WED data and gauged precipitation data as inputs.
4.1 Comparison of TRMM, WFD and gauged
precipitation datasets
Precipitation is the immediate source of water for the land
surface hydrological budget, and its uncertainty will
strongly impact the model calibration results (Li et al.
2012). The comparison between the Gauged, TRMM and
WFD precipitation datasets was conducted in four steps:
First, several statistical indices of the original TRMM and
WFD (named as TRMMO and WFDO from now on)
datasets and gauged precipitation data in the common
period of 1997–2001 were analyzed; second, the intensity
distribution and relative contribution of each rainfall class
to the total amount of rainfall are evaluated; third, seasonal
variation and correlations between TRMMO, WFDO and
gauged rainfalls are quantified, and finally, spatial patterns
of TRMMO, WFDO and gauged rainfalls were compared.
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WFDO * Gauged
TRMMO * Gauged
Fréchet distance
35.3
Gauged
3
Root mean squared error (mm/day)
TRMMO * Gauged
WFDO * Gauged
0.49
Ens
TRMMO * Gauged
0.36
6.1
5.5
TRMMO
205.7
Gauged
7.7
WFDO
7.4
TRMMO
8.7
WFDO * Gauged
Gauged
124.7
TRMMO
16
WFDO
29
-4.4 %
Max. dry spell (day)
Max. 5-day rainfall (mm/5 day)
Standard deviation (mm)
WFDO * Gauged
TRMMO * Gauged
-3.9 %
177.3/90.3
Gauged
WFDO
171.6/84.2
175.8/81.5
TRMMO
1,535
1,543.6
1,605.8
WFDO
TRMMO
Gauged
Areal mean rainfall in wet/dry season (mm/month)
Annual areal mean rainfall (mm/year)
Table 1 Comparison of statistical indices among TRMMO, WFDO and Gauged areal rainfall from 1997 to 2001
The results of basic statistical indices are shown in
Table 1. As an important and useful index to reflect the
accuracy of rainfall amount, the annual areal mean gauged
rainfall calculated by Thiessen polygon interpolation
method is 1,605.8 mm/year, and for TRMMO and WFDO
data the values are 1,543.6 and 1,535 mm/year, respectively. The average monthly rainfalls are 177.3, 175.8 and
171.6 mm/month for gauged, TRMMO and WFDO data
respectively in wet season, while the values are 90.3, 81.5
and 84.2 mm/month respectively in the dry season. The RE
of annual areal mean rainfall shows that both TRMMO and
WFDO datasets recorded slightly less rainfall than gauged
data in the region. A comparison of STDE computed from
the three datasets showed that the difference of STDE
between WFDO and gauged rainfall is smaller than the
difference between TRMMO and gauged rainfall. The
differences in the extreme rainfall which is reflected by the
maximum 5-day rainfall (Dankers and Hiederer 2008)
show that TRMMO dataset recorded 65 % more rainfall
than gauged data in storms while the WFDO data only
recorded 4 % more rainfall than gauged data. Considering
the maximum dry spell days, both TRMMO and WFDO
datasets suggest more non-rainy days compared with the
gauged data. The Ens (taking the gauged data as ‘‘Observed
data’’ and TRMMO/WFDO data as ‘‘Simulated data’’)
between gauged data and TRMMO data (denoted as EnsTG) and between gauged data and WFDO data (denoted as
EnsW-G) showed that the values of EnsW-G are higher than
the values of EnsT-G, and both are lower than 0.5 during
the period 1997–2001. The values of RMSE also show that
the WFDO dataset is better than TRMMO data in the
region. However, the Fréchet distances computed between
TRMMO/WFDO and gauged monthly areal mean rainfall
are 35.3 and 80.5 respectively, which means that the
TRMMO rainfall is more similar with the gauged rainfall
than that of the WFDO rainfall in monthly level in terms of
the Fréchet distance measure. This inconsistency of result
as compared with other statistical indices mentioned above
is probably due to the Fréchet distance easily leads to
irrelevant results if the temporal interdependence of values
is of importance, which is true in the case of rainfall graphs
and hydrographs (Chouakria-Douzal and Nagabhushan
2006; Ehret and Zehe 2011).
In summary, the above comparisons showed that in the
study region the WFDO data agreed better with the gauged
data than the TRMMO data in most of the statistical
indexes. Similar conclusions are also found in the literature. Li et al. (2013) showed that WED data provide better
results than TRMM data in driven a large scale hydrological model in Southern Africa. In another study carried
Relative error of annual areal mean rainfall
4.1.1 Basic statistical indices of original TRMM, WFD
and gauged rainfalls
80.5
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WFDO
130
2008
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out by Li et al. (2014), it is shown that WED data agreed
better with observed precipitation data than TRMM data in
India.
4.1.2 Intensity distribution of different rainfall classes
of original TRMM, WFD data and gauged rainfalls
Figure 2 shows the intensity distributions of the TRMMO,
WFDO and gauged daily precipitation in different classes
and their contributions to the total rainfall from 1997 to
2001. It is seen that non-rainy days have the largest
occurrence for the WFDO data, occurring 34 % of the total
days, but for the TRMMO and gauged data, the largest class
is 0 \ rainfall \ 3 mm, occurring more than half of the
total days. The second largest class for the WFDO,
TRMMO and Gauged data are 0 \ rainfall \ 3 mm, nonrainy days and 3 \ rainfall \ 10 mm respectively, occurring about 20–30 % of the total days. It is obviously seen
Fig. 2 Distribution of daily
precipitation in different
precipitation classes (bar
graphs) and their relative
contributions (lines) to the total
precipitation from 1997 to 2001
that both TRMMO and WFDO data observed much more
non-rainy days than gauged data, which is partly because of
the poor ability of recording trace rainfall for the TRMM
satellite and the data assimilation algorithm, observation
system and observation data applied in producing reanalysis WFD dataset (Ebisuzaki and Kistler 2000; Bengtsson
et al. 2004; Wu et al. 2005; Bengtsson et al. 2007). The
statistics for TRMMO rainfall are however different from
WFDO and gauged rainfall, with the dominant rainfall
classes contributing to the total rainfall of TRMMO data
being 10 \ rainfall \ 25 mm and 25 \ rainfall \ 50 mm,
both accounting for about 30 % of the total rainfall, while
the 10 \ rainfall \ 25 mm is the dominant class contributing more than 40 % of total rainfall for the WFDO and
gauged data. The sum of the first two classes, i.e. non-rainy
and small rain (0 \ rainfall \ 3 mm) classes, gives a
similar high percentage (approximately 65 %) of total days
for all the three datasets, but the contributions to the total
70
60
Percentage (%)
50
40
30
20
10
0
0
0-3
3-10
10-25
25-50
50-100
Range bins (mm)
Occurence_TRMM₀
Contribuon_TRMM₀
Occurence_WFD₀
Contribuon_WFD₀
Occurence_Gauged
Contribuon_Gauged
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Stoch Environ Res Risk Assess (2015) 29:2003–2020
4.1.3 Seasonal variation and correlations
between TRMMO, WFDO and gauged rainfalls
Figure 3 shows a quantitative comparison of the spatial
average of the mean monthly precipitation over Xiangjiang
River basin from TRMMO, WFDO and gauged data during
1997–2001. The figure graphically demonstrates that
WFDO data agree better with the gauged data than
TRMMO data do in 9 months except March, May and July.
It is seen that the monthly rainfall values of TRMMO data
are lower than gauged rainfall in winter (December–February) and autumn (September–November), but in spring
(March–May) and summer (June–August), the comparison
of TRMMO and gauged rainfall varies irregularly. On the
300
TRMMₒ
Precipitaon (mm/month)
250
WFDₒ
Gauged
200
150
100
400
y = 1.0267x - 8.7564
R² = 0.9269
TRMMO precipitation (mm)
300
200
100
0
0
400
100
200
Gauged precipitation (mm)
300
400
300
400
y = 0.9864x - 4.0819
R² = 0.9561
300
WFDO precipitation (mm)
rainfall amount are all below 10 % in the low rain class.
The occurrences of the middle class rainfall (3 \ rainfall \ 50 mm) decrease progressively class by class for the
three datasets, but with different contribution rates to the
total rainfall. The highest rainfall class ([50 mm) occurs
below 0.5 % of the total days and contributes 6.2, 2.1 and
3 % of the total rainfall for TRMMO, WFDO and gauged
data respectively. The TRMM satellite obviously records
more precipitation in high rainfall class than the rain
gauges which is mainly because the heavy rainfall in the
study area is dominated by the frontal rains and connective
rains, and inside these heavy frontal rains and connective
rains, rain rate is distributed heterogeneously in vertical
and horizontal directions. Therefore, the complex distribution of rain in the clouds produces large error in rainfall
volume detected by the Precipitation Radar on TRMM
satellite (Zipser and Lutz 1994). It is important to note that
the high rainfall ranges play a vital role in contributing to
the total rainfall amount. This kind of information is
essential because heavy rains cause the geographical slides
and flash floods and hence threaten the economies and
human life (Varikoden et al. 2010; Li et al. 2012).
200
100
0
0
100
200
Gauged precipitaon (mm)
Fig. 4 Scatter plots of the correlation between monthly areal mean
precipitation from TRMMO, WFDO and gauged data (1997–2001)
other hand, the monthly rainfall values of WFDO data are
always lower than Gauged rainfall except in August.
Correlation of the three datasets is shown through the
scatter plots of monthly areal TRMMO, WFDO rainfall
against gauged rainfall in Fig. 4. It is seen that there are
good linear relationships between the WFDO data and
gauged data, with the highest determination coefficient (R2)
of 0.96. The R2 value for the correlation between TRMMO
data and gauged data is 0.93. From the comparison of the
two scatter plots and the R2 values, slope and intercept of
the regression lines, it is seen that the WFDO data reproduced the observed amount of monthly rainfall slightly
better than the TRMMO data in the study region.
50
0
Jan
Feb Mar
Apr May Jun Jul
Month
Aug
Sep
Oct
Nov Dec
Fig. 3 Mean monthly precipitation in Xiangjiang River basin from
TRMMO, WFDO and gauged data (1997–2001)
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4.1.4 Spatial patterns of TRMMO, WFDO and gauged
rainfalls
Figure 5 shows the spatial distribution of relative error of
precipitation (Fig. 5a–f) and Ens computed between gauged
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Fig. 5 The spatial distribution
of Relative error of precipitation
and Ens between gauged and
TRMMO/WFDO datasets
(computed in the common
period of 1997-2001): Relative
error of precipitation between
gauged and TRMMO datasets in
a annual mean, b mean monthly
rainfall in wet season, c mean
monthly rainfall in dry season;
Relative Error of precipitation
between gauged and WFDO
datasets in d annual mean,
e mean monthly rainfall in wet
season, f mean monthly rainfall
in dry season; g is the Ens
computed by gauged and
TRMMO datasets and h is the
Ens computed by gauged and
WFDO datasets
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and TRMMO/WFDO datasets (Fig. 5g, h) interpolated by
using Ordinary Kriging method in the common period of
1997–2001. It is seen that the TRMMO data underestimate
the annual rainfall in the whole basin while the RE is larger
in western and southern parts of the basin than it is in
central-north part of the basin (Fig. 5a). In wet season, the
distribution of RE varies regularly from TRMMO data
overestimating the rainfall in the north/west to underestimating the rainfall in the south/east (Fig. 5b). In dry season, the TRMMO data underestimate the rainfall in the
whole basin and RE values increase gradually from north to
south (Fig. 5c). The distribution of RE computed by WFDO
data and gauged rainfall shows similar spatial variation
pattern in annual mean rainfall (Fig. 5d) and mean monthly
rainfall in wet season (Fig. 5e), and WFDO data vary from
overestimating rainfall in the north to underestimating in
the south gradually, while Fig. 5f shows that the WFDO
data overall underestimate the mean monthly rainfall in dry
season except a slight overestimation in the northern part of
the basin. Figure 5g, h shows that the WFDO data match
the gauged rainfall much better than the TRMMO data do.
The WFDO data match the gauged rainfall best in the
northwest of the basin with EnsW-G value reaching 0.4, and
with the worst estimates in the north. The TRMMO data
match the gauged rainfall poorly in the western and
northern parts of the basin with the EnsT-G values below
zero and the highest value of EnsT-G in eastern part of the
basin is still lower than 0.3.
The consistency test of the TRMMO and WFDO datasets
in terms of their distribution patterns (K–S test), long-term
mean values (t test) and variances (F test) is performed for
the areal average values and for each grid cells at the
significance level of 0.05. As for the areal average values,
the t test shows the differences in mean annual and seasonal values of TRMMO and WFDO data with gauged data
are insignificant at 5 % significant level. The differences in
their spatial distribution patterns and the variances are
significant at 5 % significant level. The results of consistency test for individual grid cells are summarised in
Table 2, which shows that: (1) The Null Hypotheses that
Table 2 The results of
hypothesis test among TRMM,
WFD and Gauged precipitation
in Xiangjiang River Basin
Total grid number = 36
TRMMO and gauged data belong to the same distribution
and have same variance are not rejected in only four grids;
whereas the Null Hypotheses that WFDO and gauged data
belong to the same distribution and have same variance is
therefore rejected in all grids. And (2) the student’s t test
shows two thirds of the grids (24 grids) of TRMMO rainfall
and one third of the grids (11 grids) of WFDO rainfall in the
basin have the same mean values as gauged rainfall.
4.1.5 Comparison of bias corrected TRMM and WFD
datasets with gauged data
The areal mean precipitation is one of the decisive factors
for the lumped hydrological models due to lumped models
consider a watershed as a single unit for computations, and
model inputs and watershed parameters are averaged over
this unit. However, the spatial distribution of precipitation
amounts is vital for the distributed hydrological models.
Figure 6 shows the comparison of bias corrected TRMM/
WFD precipitation data using the linear and nonlinear
correction methods (in the following part of the paper, the
linear corrected TRMM/WFD data are abbreviated as
TRMMLC/WFDLC and nonlinear corrected TRMM/WFD
data are abbreviated as TRMMNLC/WFDNLC) with gauged
rainfall for the individual grid as well as for the areal
average. It is seen that: (1) The biases existed in the
uncorrected annual mean rainfall of TRMM/WFD data
disappeared after using linear/nonlinear bias correction
algorithms both in grid and areal mean rainfall level. (2) In
case of the original and bias corrected TRMM datasets, it is
seen that the values of STDE (which were computed by the
TRMMO in each grid and the areal mean TRMMO) are
larger than the values of STDE of gauged rainfall in most
grids and in areal mean level in the catchment. The values
of Ens calculated between TRMMO and gauged rainfall in
each grid are all smaller than 0.4 while the Ens calculated
between areal mean TRMMO and areal mean gauged
rainfall is 0.52. It is evidently seen in Fig. 6a that the
nonlinear bias correction method removes the bias of the
STDE both in grid and areal mean rainfall level, and the
Hypothesis
TRMMO
versus Gauged
(1997–2005)
4
WFDO
versus Gauged
(1963–2001)
Kolmogorov–Smirnov test
The same distribution
Not the same distribution
32
36
Student’s t Test
The same mean
24
11
Not the same mean
12
25
F Test
The same variance
4
0
32
36
Not the same variance
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(a)
40%
0.4
20%
0.2
0%
0
Annual Mean of Grid
Standard Deviaon of Grid
Annual Mean of Areal Mean Rainfall
Standard Deviaon of Areal Mean Rainfall
Ens of Grid
Ens of Areal Mean Rainfall
-20%
-40%
Error Variation
-0.2
-0.4
Uncorrect
60%
Nash–Sutcliffe efficiency coefficient
0.6
Linear Correct
Nonlinear Correct
(b)
40%
0.4
20%
0.2
0%
0
-20%
-0.2
-40%
-0.4
Uncorrect
WFDNLC and areal mean gauged rainfall are observed in
Fig. 6b. The worsening of Ens values in grid level of
WFDNLC is mainly because the nonlinear bias correction
algorithm can correct the water balance (relative error)
well, but cannot correct the zero values in WFD precipitation when the gauged precipitation is not zero, since only
the non-zero records are adjusted to achieve the solution of
objective function in the correction period which could
lead to considerable variation of nonzero rainfall values.
4.2 Evaluation of streamflow simulation using TRMM,
WFD and gauged precipitation as inputs
0.6
Nash–Sutcliffe efficiency coefficient
Error Variation
60%
2013
Linear Correct
Nonlinear Correct
Fig. 6 Comparison of error variation of uncorrected, linearly
corrected and nonlinearly corrected TRMM/WFD precipitation with
gauged precipitation: a TRMM * gauged, b WFD * gauged
values of Ens computed between TRMMNLC and gauged
rainfall improved in both grid and areal mean level.
However, the linear bias correction method neither remove
the bias of the STDE nor improve the values of Ens computed between TRMMLC and gauged rainfall in both grid
and areal mean rainfall level obviously. (3) In case of the
original and bias corrected WFD datasets, it is seen that the
values of STDE (which were computed by the WFDO in
each grid and the areal mean WFDO) are smaller than the
values of STDE of gauged rainfall in most grids and in
areal mean level in the catchment. It is seen in Fig. 6b that
the nonlinear bias correction method removes the bias of
the STDE in grid level, but the STDE computed by the areal
mean WFDNLC is 15 % larger than the STDE computed by
the areal mean gauged rainfall. However, comparing with
the gauged rainfall, the linear bias correction method
removes the bias of the STDE which was computed by the
areal mean WFDLC but does not remove the bias of the
STDE in grid level. Comparing the values of Ens computed
between WFDO/WFDLC/WFDNLC and gauged rainfall in
each grid, it is seen that the linear bias correction method
improves the Ens values slightly at grid level while the
nonlinear bias correction method does not improve the Ens
values in most of the grids. Meanwhile, the worst Ens
values which were calculated by the areal mean WFDLC/
The common period of the TRMM, WFD and gauged precipitation datasets 1997–2001 was taken for model calibration. In order to make best use of the available data which
cover different record periods, the validation period for
gauged and TRMM data was 2002–2005 and the period
1993–1996 was taken for validation of WFD data. The
period 1st January 1997–31st May 1997 was taken as
warming-up period during calibration. In the SWAT model,
the catchment was delineated into 55 sub-basins and 555
HRUs (Hydrological Response Units) depending on elevation, soil types and land-use types. In model calibration, the
Nash–Sutcliffe efficiency coefficient was taken as objective
function, the Genetic Algorithm and SUFI2 (Sequential
Uncertainty Fitting version 2) Algorithm provided by
SWAT CUP were used for calibrating the Xinanjiang Model
and SWAT Model respectively. All the 15 parameters of
Xinanjiang Model and the 12 most sensitive parameters of
SWAT Model (ranked by CN2, SOL_AWC, GWQMN,
ESCO, GW_DELAY, ALPHA_BF, SOL_BD, SOL_K,
REVAPMN, SLSUBBSN, SURLAG and GW_REVAP)
were selected for calibrating the models (Table 3). The same
initial values and range of parameters were applied in calibrating the different data based models.
4.2.1 Evaluation of the daily step simulated streamflow
The evaluation of model simulation results at daily time
step is performed on two aspects in this study and results
are presented in the following three paragraphs in this
section, i.e., statistical quality measures of Ens and RE, and
graphical comparison of observed and model simulated
daily discharges.
Table 4 shows the Ens values and Relative Error (RE) for
the Xinanjiang and SWAT Models’ simulation results. In
case of Ens values, it is seen that: (1) The gauged precipitation data based hydrological models performed best
while the performances of the TRMMO and WFDO precipitation based models were far from being satisfactory at
daily time step. (2) The Ens values using the linearly corrected TRMM and WFD data as input improved from 0.62
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Table 3 The calibrated parameters of Xinanjiang model and SWAT model
Xinanjiang model
Name
Description
Um
Averaged soil moisture storage capacity of the upper
layer (mm)
Lm
SWAT model
Name
Description
Parameter
range
10–60
ALPHA_BF
Base flow recession factor (days)
0-1
Averaged soil moisture storage capacity of the lower
layer (mm)
50-160
GW_DELAY
Ground water delay (days)
0-15
Dm
Averaged soil moisture storage capacity of the deep
layer (mm)
100-180
GW_REVAP
Ground water revaporation
coefficient
0-0.5
B
Exponential parameter with a single parabolic curve,
which represents the non-uniformity of the spatial
distribution of the soil moisture storage capacity
over the catchment
0.3-0.7
GWQMN
Threshold depth for ground water
flow to occur (mm)
0-10
Im
Percentage of impervious and saturated areas in the
catchment
Ratio of potential evapotranspiration to pan
evaporation
0-0.1
SLSUBBSN
Average slope length (m)
0-100
0.1-1.2
SOL_BD
Moist bulk density (g/cm3)
0-25
Coefficient of the deep layer that depends on the
proportion of the basin area covered by vegetation
with deep roots
Areal mean free water capacity of the surface soil
layer, which represents the maximum possible
deficit of free water storage (mm)
0.1-0.3
SOL_K
Saturated hydraulic conductivity
(mm/hour)
0-10
30-60
REVAPMN
Threshold depth for revaporation to
occur (mm)
0-100
Ex
Exponent of the free water capacity curve
influencing the development of the saturated area
1-2
ESCO
Soil evaporation compensation factor
0-1
Kg
Outflow coefficients of the free water storage to
groundwater relationships
0.9-0.99
SOL_AWC
Available water capacity (m/m)
0-1
Ki
Outflow coefficients of the free water storage to
interflow relationships
0.8-0.9
CN2
Curve number
Cg
Recession constants of the groundwater storage
0.3-0.7
SURLAG
Surface runoff lag coefficient (days)
Ci
Recession constants of the lower interflow storage
0.3-0.7
Ke
Parameter of the Muskingum method
0.1-4
Xe
Parameter of the Muskingum method
0.1-4
K
C
Sm
Parameter
range
and 0.79 to 0.65 and 0.81 in the Xinanjiang Model as
compared to the Ens values using uncorrected data. The Ens
values improved from 0.56 and 0.71 to 0.63 and 0.73 in
SWAT Model respectively. However, the Ens values using
nonlinear correction method show slightly worse results in
case of WFDNLC data in both models. Specifically, the Ens
values using the non-linearly corrected TRMM and WFD
data as input changed from 0.62 and 0.79 to 0.67 and 0.76
in the Xinanjiang Model, and changed from 0.56 and 0.71
to 0.64 and 0.66 in SWAT Model respectively as compared
to the Ens values using uncorrected data. (3) Similar conclusions can be drawn for the validation period. Both linear
and nonlinear correction methods improved the simulation
results in the validation period for both models in the case
of TRMM data, while in the case of WFD data linear
correction method improved model simulation results but
opposite results were obtained for the nonlinear correction
method. And (4) in summary, WFD data, both original and
bias corrected, suggest better results than TRMM data in
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-25–30
0-20
the calibration period at daily time step for both models;
nonlinear correction method performs better model simulation results than linear correction method for both models
in the case of TRMM data; as for WFD data, linear correction method provides better model simulation results
than the nonlinear correction method for both models.
In case of Relative Error values, it is seen from Table 4
that (1) comparing simulations with uncorrected data the
values of RE produced by TRMMLC reduced in both
models, and the values of RE produced by WFDLC reduced
in Xinanjiang model and increased in SWAT model in the
calibration period. In the validation period, the values of RE
increased/decreased in the case of TRMMLC/WFDLC for
Xinanjiang model, while opposite is true for SWAT model.
(2) The RE values produced by TRMMNLC increased/
decreased in the case of Xinanjiang/SWAT models in both
calibration and validation periods, while opposite is true in
case of using WFDNLC data. Above comparison reveals
that, combining both quality measures of Ens and RE, the
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Table 4 Comparison of model performance in daily step simulation based on the input of TRMM, WFD and gauged precipitation
Gauged
TRMM
Uncorrected
Xinanjiang model
SWAT model
WFD
Linear
corrected
Linear
corrected
0.96
0.62
0.79
0.81
-1.8 %
-5.9 %
-3.6 %
-9.6 %
1.8 %
0.8 %
Validation period
Ens
Relative error
0.94
-3.9 %
0.85
5.1 %
0.72
16.5 %
0.78
16.6 %
0.75
-3.3 %
Calibration period
Ens
0.9
0.56
0.63
Relative error
1.3 %
7.5 %
5.2 %
Validation period
Ens
0.89
0.6
0.67
0.72
16 %
11.6 %
Ens
Relative error
-0.7 %
20.4 %
0.67
Uncorrected
Relative error
Calibration period
0.65
Nonlinear
corrected
0.64
-6.3 %
0.71
-1.2 %
0.74
-2.4 %
Nonlinear
corrected
0.76
1%
0.71
2.1 %
0.73
0.66
-1.7 %
11 %
0.68
0.72
0.62
-1 %
1.9 %
-11.5 %
Fig. 7 Comparison of the observed and simulated daily hydrographs at Xiangtan station: a TRMM based Xinanjiang model, b TRMM based
SWAT model, c WFD based Xinanjiang model, d WFD based SWAT model
WFD data is relatively better suited for the daily step
hydrological simulation than the TRMM data in this study
basin.
For illustrative purpose, Fig. 7 shows the comparison of
the observed and simulated daily stream flow hydrographs
of year 2000 in the calibration period in Xiangtan gauge
which are produced by the original and bias corrected
TRMM and WFD rainfall datasets in Xinanjiang Model
and SWAT Model respectively. It is seen that: (1) The
WFDLC daily rainfall based Xinanjiang Model matches the
observed discharge well, followed by the WFDO daily
rainfall based Xinanjiang Model. Compared with the WFD
data based hydrological models, TRMM data perform
poorly in simulations. The simulated hydrographs show a
tendency for the models to misestimate the extreme peak
flows. This can be attributed to the low precision of original and mean monthly bias corrected TRMM and WFD
rainfall data in matching the maximum rainfalls. And (2)
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Table 5 Comparison of model performance in monthly step simulation based on the input of TRMM and WFD precipitation
Gauged
TRMM
WFD
Uncorrected
Xinanjiang model
SWAT
model
Ens
Linear
corrected
Nonlinear
corrected
Uncorrected
Linear
corrected
Nonlinear
corrected
Calibration period
0.98
0.76
0.81
0.82
0.92
0.94
Validation period
0.98
0.9
0.89
0.95
0.94
0.95
0.92
-2.8 %
0.95
-3.9 %
0.72
2.5 %
0.81
2.3 %
0.73
-0.4 %
0.85
-0.6 %
0.88
-0.3 %
0.74
0.94
0.83
0.88
0.85
0.9
0.92
0.81
9.6 %
4.4 %
1.8 %
-1.1 %
-0.1 %
Relative error
Ens Calibration period
Validation period
Relative error
-1.9 %
0.89
1.3 %
Fig. 8 Comparison of the relative error of mean monthly runoff
simulation driven by original and bias corrected TRMM and WFD
rainfall data in a TRMM datasets based Xinanjiang model, b TRMM
datasets based SWAT model, c WFD datasets based Xinanjiang
model and d WFD datasets based SWAT model
the corrected and uncorrected TRMM/WFD data (except
WFDNLC based SWAT Model) all show lower discharge
than observed discharge in dry season while they show
higher values than observed discharge in the wet season in
both hydrological models.
and the Ens values are 0.98, 0.98, 0.95 and 0.94 for the
calibration and validation periods of the Xinanjiang and
SWAT models respectively. The relative errors are all
smaller than 3 %. As for the results of TRMM and WFD
datasets, the following results are obtained: (1) the largest RE
value is reduced from 9.6 % for the case of uncorrected
TRMM data to 4.4 % for linearly corrected TRMM data.
The RE values for all other bias corrected cases are smaller
than 3 %. (2) The Ens values of WFDLC based Xinanjiang
Model are the highest, achieving 0.94 and 0.95 in calibration
period and validation period respectively. (3) The nonlinearly corrected datasets generally perform worse results than
linearly corrected datasets with an exception of TRMM data
for Xinanjiang model. And (4) it can be concluded that both
4.2.2 Evaluation of the monthly step simulated streamflow
To demonstrate the suitability of the TRMM and WFD
datasets in monthly step hydrological simulation, Table 5
shows the precision of the original and bias corrected
TRMM and WFD precipitation data based models for
monthly streamflow simulation. It is seen that the gauged
data based models still gave the best model performances,
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TRMM and WFD rainfall data can be used as model input
for monthly flow simulation in the study region and again
WFD data perform better than TRMM data.
The seasonal biases of the simulated discharges using
observed precipitation and original and bias corrected
TRMM/WFD datasets are compared for both Xinanjiang
and SWAT models (Fig. 8). It can be seen from the figure
that: (1) In case of original and bias corrected TRMM data
based Xinanjiang Model (Fig. 8a), the TRMMNLC data
produce the best simulation results in general (all
|RE| \ 10 % except in April). The TRMMLC based model
misestimated the flow obviously (|RE| [ 10 %) in most of
the months except January, May, June and September. (2)
In case of original and bias corrected TRMM data based
SWAT Model (Fig. 8b), compared with the TRMMO based
model, TRMMLC data considerably improve the flow
simulation in February, August, September, October and
November. Meanwhile, the TRMMNLC data considerably
improve the flow simulation in January, April, August,
October, November and December. (3) In case of original
and bias corrected WFD data based Xinanjiang Model
(Fig. 8c), the WFDLC/WFDNLC data obviously produce
larger error than the WFDO data in flow simulation in
January-April and November and the WFDNLC data also
considerably overestimate the flow in the model in September. However, in June–September (raining season), the
WFDLC based model estimates the flow very well
(|RE| \ 5 %). (4) In case of original and bias corrected
WFD data based SWAT Model (Fig. 8d), the WFDO based
SWAT model underestimates the streamflow obviously
except in May and July. The WFDLC based model considerably improves the flow simulation compared with the
WFDO data based model in most months (except May, July
and November). Meanwhile, the RE values of flow produced by the WFDNLC based model improve in January,
June, August–October and December obviously compared
with the RE calculated by the WFDO data based model, but
considerably worsen RE values can also be observed in
March, may, July and November.
In summary, above results reveal that the linearly corrected WFD rainfall data give the best prospect for monthly
step streamflow simulation in both models. However, the
nonlinearly corrected TRMM dataset has the potential to be
a suitable data source for the data-poor or ungauged basins
due to its real-time updating, particularly for the large-scale
or meso-scale basins in remote locations.
2017
corrected TRMM and WFD rainfall for discharge simulation using lumped Xinanjiang Model and distributed
SWAT Model in Xiangjiang River Basin, south China.
The following conclusions can be drawn from the results
of this study:
(1)
(2)
(3)
5 Conclusions
This paper compared the differences between TRMM and
WFD rainfall datasets with gauged rainfall data at daily and
monthly time steps and evaluated the accuracy of bias
(4)
The differences of the mean annual and seasonal
areal precipitation calculated from the gauged precipitation and the two original global datasets
(TRMMo and WFDo) are in general within the
acceptable value of less than 10 %, and the linear
correlation between the monthly gauged precipitation and TRMMo and WFDo data as reflected by the
interception, slope and R2 are in general very good.
However, the spatial differences as reflected by the
results of K–S test and F test on each grid for
distribution pattern and variance are found to be
significant between the gauged and the two global
datasets (TRMMo and WFDo) in most grids, while
the student’s t test shows two thirds (one third) of the
grids of TRMMO (WFDo) rainfall in the basin have
the same mean values as gauged rainfall. The
differences in maximum dry spell are significant
between the two global datasets and the gauged
rainfall, while the difference in maximum 5-day
rainfall is significant between TRMMo and gauged
values.
The bias correction methods are able to remove the
biases existed in the mean values of TRMMo and
WFDo datasets. The nonlinear bias correction
method gives good results in correcting the standard
deviation values in TRMM/WFD data at grid level.
The Ens values of TRMM data improved after
nonlinear bias correction in most of the grids as well
as in the areal mean rainfall. The linear bias
correction method gives good results in correcting
the standard deviation of areal mean WFD data. But
the Ens values of WFD data do not improve in both
grid and areal mean level after linear or nonlinear
bias correction.
The comparison of model simulation results using
gauged precipitation and the two original global
datasets (TRMMo and WFDo) as inputs to the two
hydrological models reveals that at daily time step
both TRMMo and WFDo produce much worse
results than using observed precipitation data in
terms of Nash–Sutcliffe efficiency Ens and relative
error, RE. Both linearly and nonlinearly bias corrected TRMM and WED data do not significantly
improve the model simulation results at daily time
step.
At monthly time step the modelling study shows that
both TRMM and WFD data produce acceptable
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Stoch Environ Res Risk Assess (2015) 29:2003–2020
model simulation results in terms of both Nash–
Sutcliffe efficiency (Ens [ 0.7 for original and
nonlinear corrected TRMM/WFD data, Ens [ 0.8
for linear corrected TRMM/WFD data) and relative
error (all |RE| \ 10 %). Linear correction method
produces better simulation results than nonlinear
correction method and the best results were found
from Xinanjiang Model using linearly corrected
WFD data without considering gauged data based
models.
In general, it can be said that the reanalysis WFD
data matches the gauged precipitation better and
produce slightly better model simulation results in
the region than the TRMM data, and the lumped
Xinanjiang model performs better than the distributed SWAT model at both monthly and daily time
steps.
However, it should be noted that:
(1)
(2)
Several shortcomings still exist in using global
datasets in hydrological simulations, as TRMM and
WFD overestimate or underestimate the rainfall in
different years and areas, and have a generic failure
to detect the small and extreme rainfall which limit
the precision of discharge simulation at daily time
steps and in hydrological applications including
drought and flood forecasting. These shortcomings
in TRMM and WFD datasets indicate that further
efforts of evaluating the satellite-based and reanalysis datasets need to be continued in more areas with
different scales and climatic conditions and using
hydrological models with different spatial–temporal
resolutions.
The spatial autocorrelation is not considered due to a
dense network of rain gauge stations is available in
this study to remedy the bias of TRMM and WFD
datasets grid by grid. However, when the reanalysis/
satellite based datasets are used in the basins without
such densely distributed rain gauges to interpolate
grid rainfall, the Biased Sentinel Hospitals-Based
Area Disease Estimation (B-SHADE) model which
takes into account prior knowledge of geographic
spatial autocorrelation and non-homogeneity of
target domains, remedies the biased sample, and
maximizes an objective function for best linear
unbiased estimation (BLUE) of the regional mean
(total) quantity (Wang et al. 2011) can be adopted as
an alternative choice to correct the bias. Unlike the
bias correction methods applied in the study that
simply considers the statistical relationship between
data series, the B-SHADE model considers coexistence of homogeneity and heterogeneity of the
variant in land surface (Wang et al. 2009, 2010).
123
(3)
Meanwhile, the B-SHADE model uses correlation
between stations and selecting several observation
stations of greatest correlation that can better reflect
the reality (Xu et al. 2013).
Furthermore, the newly released WATCH-ForcingData-ERA-Interim (http://www.eu-watch.org/data_
availability), a dataset produced post-WATCH
using WFD methodology applied to ERA-Interim
data, extends the meteorological forcing dataset into
early twenty first century (1979–2012) and provides
an alternative dataset for ungauged or sparsely
gauged basins for hydrological analysis and modelling. The usefulness of this dataset will be evaluated
in the on-going research.
Acknowledgments The authors would like to thank the two anonymous reviewers and the editor who helped us improving the quality
of original manuscript. The authors would like to thank the SinoTropia Project (Watershed EUTROphication management in China
through system oriented process modelling of Pressures, Impacts and
Abatement actions) who provided the soil distribution map used in
SWAT Model. This work was supported by grants from the National
Natural Science Foundation of China (Grant No. 61301063).
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