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\begin{document}

\title{Spatiotemporal evolution of chlorophyll-a concentration from MODIS data
inversion in the middle and lower reaches of the Hanjiang River, China}



\author[1]{zhuo Chen}%
\author[1]{ming Dou}%
\author[2]{Rui Xia}%
\author[1]{Guiqiu Li}%
\author[1]{Lisha Shen}%
\affil[1]{Zhengzhou University}%
\affil[2]{Chinese Research Academy of Environmental Sciences}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The global construction of water projects has led to a clear trend of
river and lake reservoir formation, spurring increasingly serious
ecological environmental deterioration, especially that caused by the
frequent occurrence of water blooms. Because of monitoring technology
limitations, monitoring the algae content index in water has lagged
behind the conventional water quality index, which makes sample
monitoring too sparse in many rivers and the monitoring data incoherent,
so it cannot truly reflect the evolution of water eutrophication. With
moderate resolution imaging spectroradiometer (MODIS) remote sensing
data monitoring, continuous chlorophyll-a observation data can be
collected effectively. This method has important guiding significance
for the early warning and control of water blooms. This study considers
the middle and lower reaches of the Hanjiang River in China, based on
the current remote sensing communication technology, MODIS remote
sensing data, and statistical methods and measured chlorophyll-a
concentration correlation analysis. Through the use of the trial and
error method to establish the band ratio model and BP neural network
model, two types of model errors were compared to determine the optimal
algorithm settings for the middle and lower reaches of the Hanjiang
River chlorophyll-a inversion. Subsequently, the algorithm model for
2000 to 2011 in the middle and lower reaches of the Hanjiang River
chlorophyll-a concentration inversion and the results of the inversion
analysis of spatiotemporal evolution characteristics we used to
determine the influence of various environmental factors on the
chlorophyll-a concentration change.%
\end{abstract}%



\sloppy


\textbf{Spatiotemporal evolution of chlorophyll-a concentration from
MODIS data inversion in the middle and lower reaches of the Hanjiang
River, China}

Zhuo Chen \textsuperscript{1}, Ming Dou \textsuperscript{1,2 *}, Rui Xia
\textsuperscript{3,*}, Guiqiu Li\textsuperscript{1,2}, Lisha
Shen\textsuperscript{1,2}

\textsuperscript{1} School of Water Conservancy Science and Engineering,
Zhengzhou University, Zhengzhou, 450001, China

\textsuperscript{2} School of Ecology and Environment, Zhengzhou
University, Zhengzhou, 450001, China

\textsuperscript{3} State Key Laboratory of Environmental Criteria and
Risk Assessment, Chinese Research Academy of Environmental Sciences,
Beijing, 100012, China

\textbf{Corresponding Author:} Ming Dou E-mail
address:\emph{dou\_ming@163.com}

\textbf{Abstract} : The global construction of water projects has led to
a clear trend of river and lake reservoir formation, spurring
increasingly serious ecological environmental deterioration, especially
that caused by the frequent occurrence of water blooms. Because of
monitoring technology limitations, monitoring the algae content index in
water has lagged behind the conventional water quality index, which
makes sample monitoring too sparse in many rivers and the monitoring
data incoherent, so it cannot truly reflect the evolution of water
eutrophication. With moderate resolution imaging spectroradiometer
(MODIS) remote sensing data monitoring, continuous chlorophyll-a
observation data can be collected effectively. This method has important
guiding significance for the early warning and control of water blooms.
This study considers the middle and lower reaches of the Hanjiang River
in China, based on the current remote sensing communication technology,
MODIS remote sensing data, and statistical methods and measured
chlorophyll-a concentration correlation analysis. Through the use of the
trial and error method to establish the band ratio model and BP neural
network model, two types of model errors were compared to determine the
optimal algorithm settings for the middle and lower reaches of the
Hanjiang River chlorophyll-a inversion. Subsequently, the algorithm
model for 2000 to 2011 in the middle and lower reaches of the Hanjiang
River chlorophyll-a concentration inversion and the results of the
inversion analysis of spatiotemporal evolution characteristics we used
to determine the influence of various environmental factors on the
chlorophyll-a concentration change.

\textbf{Keywords:} MODIS; chlorophyll-a; algal blooms; Hanjiang River

\section*{1. INTRODUCTION}

{\label{introduction}}

Algal blooms are a prominent ecological and environmental problem (Yang
et al., 2012), occurring frequently in water bodies worldwide, affecting
not only water quality and aquatic organism survival, but also drinking
water safety and human life and health in serious cases. A key factor
causing this problem is water body eutrophication. A high level of
eutrophication in a water body induces blue-green algae to proliferate
and form a blue-green algae film on the water surface, thereby polluting
the water body. Algal bloom outbreaks usually occur in inland lakes,
reservoirs, or ponds (Huo et al., 2019), and outbreaks in large rivers
occur only rarely. However, with the construction and operation of water
conservancy projects at home and abroad, the hydrological situation of
large rivers has transformed. Compared with lakes and reservoirs, their
hydrological conditions are more complex and more prone to water blooms,
so it is more meaningful to strengthen the monitoring of algal blooms in
large rivers and mobile water bodies for water environmental protection.

With the improvement of communication technology, moderate resolution
imaging spectroradiometer (MODIS) remote sensing satellite data are
widely used in the fields of environmental monitoring, resource
investigation, urban planning, and other fields. MODIS is a large space
remote sensing instrument (Naghdi et al., 2020) developed by NASA, which
provides long-term Earth observation data, including atmospheric
information, global coverage, multi-spatial resolution, and
multi-spectral images, and can provide support for understanding global
climate and the impact of human activities on climate (Liu et al.,
2020).

As the main component of chlorophyll in algae, the distribution of
chlorophyll-a concentration can better reflect the degree of algal bloom
in water bodies (Brigitte, 2019), and there are obvious absorption
valleys in the blue band (near 442 nm) (Han et al., 1994(a)) and the red
band (near 665 nm) (Han et al., 1994(b)), and obvious reflection peaks
in the infrared band (near 700 nm) (Gitelson et al., 1992), showing
certain characteristic spectra in water. The high or low concentration
of chlorophyll-a leads to a significant change in the reflectance
spectral characteristics of a water body (Jiang et al., 2020).
Therefore, the indirect estimation of chlorophyll-a concentration from
satellite reflectivity data is more dynamic, periodic, and real-time
than traditional manual monitoring, which is suitable for inland waters
with complex optical characteristics. Furthermore, the inversion model
of chlorophyll-a concentration can be used to interpolate the measured
chlorophyll-a concentration according to the remote sensing data, which
provides support for further studies.

The models often used to invert water quality parameter concentrations
from remote sensing data are mostly empirical and semi-empirical models
(Shu et al., 2000; Hu et al., 2009; Tebbs et al., 2013; Telesca et al.,
2018; Cannizzaro et al., 2019), which have certain shortcomings in
inversion accuracy and generalizability and have poor fault-tolerance
(Tao et al., 2018). For this reason, some researchers (e.g., Park et
al., 2015; Harvey et al., 2015), have continuously improved the
inversion accuracy and generalizability by establishing a waveband ratio
model and introducing nonlinear relational optimization methods such as
neural network methods and genetic algorithms into water quality
parameter inversion.

Based on this, this study adopts the band ratio model as well as the BP
neural network model, and takes a typical river section in the middle
and lower reaches of the Hanjiang River in China as the study area. It
uses the measured water quality data and MODIS satellite remote sensing
data, establishes the band ratio model and BP neural network model
through an empirical method, and determines the optimal inversion
algorithm for chlorophyll-a in the middle and lower reaches of the
Hanjiang River by comparing model errors. The chlorophyll-a
concentrations in the middle and lower reaches of the Hanjiang River
from 2000 to 2011 were inverted using this model, and the temporal and
spatial characteristics of chlorophyll-a concentration in the water body
were analyzed.

\section*{2. MATERIALS AND METHODS}

{\label{materials-and-methods}}

\subsection*{2.1 Study area}

{\label{study-area}}

The Hanjiang River (106\selectlanguage{ngerman}°12'E--114°14'E, 30°08'N--34°11'N) is a tributary
of the Yangtze River in China, with a total length of 1577 km and a
drainage area of 15. 9 × 104 km\textsuperscript{2} (\textbf{Fig. 1} ).
The main stream of the Hanjiang River is 652 km, and the total drainage
area of Hubei Province is 4.6 × 104 km\textsuperscript{2}, flowing
through Shiyan, Xiangyang, Jingmen, Xiaogan, Wuhan City, and other
cities. The regional economy is developed, and it is an important grain
production base in China as well as a new industrial and automobile
industrial base in Hubei Province (Kuman et al., 2014).

{[}Insert Fig. 1. here{]}

As the largest tributary of the Yangtze River and an important water
source for the Middle Route Project of the South-to-North Water
Transfer, the water quality of the middle and lower reaches of the
Hanjiang River has gradually deteriorated since the 1990s. The main
reason is that nutrients, such as nitrogen and phosphorus in the water
body, exceed the standard (Stephen et al., 2016; Cheng et al., 2019),
resulting in a high degree of eutrophication in the water body, and
repeated outbreaks of water blooms, during which the water body appears
brown and emits a fishy and pungent odor (\textbf{Fig. 2} ). Water
blooms in the Hanjiang River occur mostly during the dry period
(February to March) in late winter and early spring, and the duration
varies from half a month to one month. According to incomplete
statistics (Xin et al., 2019; Chen et al., 2007; Lu et al., 2000) of
various studies (\textbf{Table 1} ), the water bloom frequency in the
Hanjiang River increased after 2000, with diatom blooms dominating in
the Hanjiang River (Kuang et al., 2000). Previous studies have mostly
considered the dominant species to be Chlorella (Liang et al., 2012),
with occasional cases of green algae as the dominant species. However,
according to a different study (Zheng et al., 2009; Yin et al., 2011),
the dominant species of the Hanjiang water bloom belongs to the genus
Corynebacterium. The rapid growth of algae is accompanied by an increase
in chlorophyll-a concentration, so it is meaningful to reveal the
phenomenon of water blooms in the middle and lower Hanjiang River by
studying the change in chlorophyll-a concentration in the middle and
lower Hanjiang River.

{[}Insert Fig. 2. here{]}

{[}Insert Table 1. here{]}

Meanwhile, the transfer of water from the South-North Water Transfer
Project has transformed the hydrological situation in the middle and
lower reaches of the Hanjiang River, and has tended to aggravate the
phenomenon of water blooms (Kuo et al., 2018; Zhou et al., 2017).
According to Dou and Xie et al. (Xie et al., 2004; Xie et al., 2005),
the impact of the South-North Water Transfer Project on water blooms in
the middle and lower reaches of the Hanjiang River is mainly reflected
in hydrological factors such as flow rate and velocity. Therefore, it is
important to analyze the effects of hydrological factors (including flow
rate and velocity) on the changes in chlorophyll-a concentration in the
middle and lower reaches of the Hanjiang River to reveal the mechanism
of hydrological factors affecting water blooms in the Hanjiang River and
to study the water bloom conditions in eutrophic rivers.

\subsection*{2.2 Actual data and MODIS data
acquisition}

{\label{actual-data-and-modis-data-acquisition}}

The measured data in the study include two parts: one is the water
quality data, including chlorophyll-a concentrations monitored along
three cross-sections, Baihezui, Qinguankou, and Zongguan (monitored in
February, March, April, September, and October from 2009 to 2013, and
monitored three times a month), and total nitrogen (TN) and total
phosphorus (TP) concentrations monitored at the Zongguan cross-section
(monitored from 2003 to 2009). The second part is the hydrological data,
which includes the flow, water level, flow rate, and water temperature
data monitored in the Xiantao and Zongguan sections, from 2000 to 2011
(the above data are from the Chinese Research Academy of Environmental
Sciences).

The MODIS remote sensing satellite has seven spectral bands from 469 to
2130 nm with a spatial resolution of 500 m. Several (75) MODIS remote
sensing images synchronized with the measured water quality data from
2009 to 2013 were selected, and 64 valid remote sensing images,
accounting for 85.33\% of the total images, were filtered after
rejection (NASA).

\subsection*{2.3 MODIS data
preprocessing}

{\label{modis-data-preprocessing}}

The remote-sensing images were preprocessed using ENVI5.3 software (from
Exelis Visual Information Solutions Company). Geometric correction was
first performed to reduce geometric distortion error. The ENVI automatic
orthorectification tool RPC Orthorectification Workflow (Guo, 2002) was
used for orthorectification of remote sensing images.

After geometric correction, the MODIS data saved the (Digital Number)DN
value, whereas the chlorophyll-a concentration inversion used absolute
radiation brightness and reflectivity. For follow-up studies, the DN
value needs to be converted to absolute radiance brightness and
reflectance, a step called radiometric calibration, which is calculated
in ENVI according to Eqns. (1) and (2) (Lv et al., 2009; Carol et al.,
2006).

To obtain the reflectance of ground objects more accurately (Feng, 2021;
Zheng et al., 2004), it is necessary to correct the atmosphere in the
image to eliminate the influence of illumination, water vapor, and other
factors on the reflectivity spectrum of ground objects. The FLAASH
atmospheric correction module in ENVI was used to correct the images.

The normalized difference water index (NDWI) (Gao, 1996; Choung et al.,
2016) was adopted for extraction in the studied water areas. Compared
with the traditional normalized difference vegetation index (NDVI)
method, NDWI is less sensitive to atmospheric influence, less affected
by the atmosphere, and more accurate for water extraction. The formula
for calculating the NDWI is as follows (3):

The reflectivity is green light reflectivity and near-infrared
reflectivity.

\subsection*{2.4 MODIS data and chlorophyll-a concentration inversion
method}

{\label{modis-data-and-chlorophyll-a-concentration-inversion-method}}

Chlorophyll-a concentration has a significant effect on the spectral
characteristics of water bodies and can be used to establish a link with
the spectral characteristics of remote sensing images on this basis (Le
et al., 2010; Darecki et al., 2003). The empirical method is a
chlorophyll-a inversion method that is currently used more often, and
the fitted construction models are mainly single-band, band ratio, and
neural network models, whose processes are relatively simple and
feasible (Chen et al., 2007).

In this study, first the downloaded MODIS data were corrected and
processed to extract the band reflectance, and the water quality data of
the actual measured water bodies were correlated with the synchronized
remote sensing reflectance data, and the reflectance data of the band or
the combination of bands with high correlation were screened out and
fitted to construct a model (Anatoly, 2008).

\subsubsection*{\texorpdfstring{\emph{2.4.1 Band ratio
model}}{2.4.1 Band ratio model}}

{\label{band-ratio-model}}

The band ratio model combines the existing bands and selects the
combination that is most relevant to the measured data to perform the
inversion modeling (Le et al., 2009). According to the correlation
analysis, the interference of the atmosphere on electromagnetic waves
and the influence of the smooth water surface on microwaves can be
reduced by combining wave bands. In this study, from the pre-processed
MODIS remote sensing images, the reflectance of seven bands from B1 to
B7 was extracted, and then the statistical analysis software SPSS (form
BIM Company) was used to combine each band to obtain 73 band combination
patterns. There were 21 modes for the division of two bands (B1/B2), 20
modes for subtraction of two bands (B1-B2), 15 modes for three bands
(B1/(B2 B3)), 10 modes for four bands (B1/(B2 + B3 + B4)), and seven
modes for natural logarithm (ln (B1)). Then, a correlation analysis
between the band combination model and the measured concentration of
chlorophyll-a was performed, and a function model was established.

\subsubsection*{\texorpdfstring{\emph{2.4.2 BP neural network
model}}{2.4.2 BP neural network model}}

{\label{bp-neural-network-model}}

The BP neural network method is currently one of the more widely used
artificial neural networks, with the characteristics of forward signal
propagation and backward error propagation (Le et al., 2000), which has
the functions of self-adaptation, self-learning, self-organization, and
nonlinear mapping, which are suitable for simulating various intricate
nonlinear relationships. It has been proven that a network with
deviation and at least one S-shaped implicit layer plus a linear output
layer can approximate any rational function (Zhu et al., 2017).

Because of the complex optical properties in the water column and the
many factors affecting chlorophyll-a concentration, the relationship
between reflectance and chlorophyll-a concentration cannot be accurately
reflected using a simple linear model (Feng et al., 2021). According to
its characteristics, the BP neural network can better solve the problem
of complex nonlinear relationships between reflectance and chlorophyll-a
concentration, as shown in the schematic diagram in \textbf{Fig. 3} ,
where\(\ X_{1},\ X_{2}\), \ldots{}\(X_{n}\) are the input and
output values of the BP neural network model. In this study, the
combination of the high correlation of wave reflectance with
chlorophyll-a concentration can be used as input
factors\(\ (X_{1},\ X_{2}\), \ldots{}\(,\ X_{n}\)) for the BP neural
network input layer, and chlorophyll-a concentration of the BP neural
network. The combination of band reflectivity and concentration of
chlorophyll-a was used to adjust the weights and thresholds of the
network to generate the model.

{[}Insert Fig.3. here{]}

\subsection*{\texorpdfstring{\emph{2.5 Model validation
method}}{2.5 Model validation method}}

{\label{model-validation-method}}

To test the model precision and make the inverse model more accurate,
this study evaluates model accuracy by the decision coefficient R2,
relative error RE, and root-mean-square error RMSE. The formulas are
(4), (5), and (6). The predicted value was the measured value.

\section*{3. RESULTS AND ANALYSIS}

{\label{results-and-analysis}}

Based on the inversion method of MODIS data and chlorophyll-a
concentration proposed in Section 2.4, the inversion of chlorophyll-a
concentration in the middle and lower reaches of the Hanjiang River was
performed, and an analysis of spatial and temporal variation in
chlorophyll-a concentration, interannual variation in chlorophyll-a
concentration distribution area, and interannual variation in
chlorophyll-a concentration values in typical years were conducted based
on the inversion results. The specific results and analyses are as
follows.

\subsection*{3.1 Results of correlation
analysis}

{\label{results-of-correlation-analysis}}

The correlation analysis of reflectance with the measured chlorophyll-a
concentration values was calculated, as shown in \textbf{Table 2} . The
B5 reflectance was significantly correlated with the measured
chlorophyll-a concentration, and the Pearson correlation coefficient was
-0.645, with a significance of 0.009. The B2 and B6 bands were
significantly correlated with the measured concentration of
chlorophyll-a, and the Pearson correlation coefficients were -0.55 and
-0.515, with significance of 0.033 and 0.049, respectively.

{[}Insert Table 2. here{]}

{[}Insert Table 3. here{]}

The results of the correlation analysis of 73 combinations of wavelength
reflectance and measured chlorophyll-a concentration values obtained
using the rule of four are presented in Table 3. The results showed that
the B7/B5, B7-B6, B7-B5, B5-B4, B5-B1, B4-B2, B2-B1, B7/(B6 B5), B4/(B3
+ B2), and LN (B5) band combinations showed good correlation, with
Pearson correlation coefficients of 0.679, 0.610, 0.609, -0.621, -0.688,
0.635, -0.730, 0.774, 0.646, and -0.712, respectively (\textbf{Table 3}
).

\subsection*{3.2 Modeling results}

{\label{modeling-results}}

According to the results in Section 3.1, the correlation between the
B7/(B6-B5) band combination and the measured chlorophyll-a concentration
value was high, and the Pearson correlation coefficient was 0.744, which
is significant. Therefore, the B7/(B6-B5) band reflectance was selected
as an independent variable, and the measured chlorophyll-a concentration
was a dependent variable. In SPSS software, eleven common function
models, including the linear, logarithmic, inverse, quadratic, and cubic
function models were selected to construct the model among variables.
The calculation results are shown in \textbf{Fig. 4} and \textbf{Table
4.}

{[}Insert Fig.4. here{]}

{[}Insert Table 4. here{]}

To select the most suitable model, the coefficient of determination
R\textsuperscript{2} and the significance of the eleven models are
calculated in \textbf{Table 4} , which shows that the cubic function
model coefficient of determination R\textsuperscript{2} is 0.677, and
the significance is 0.001. Compared with the other ten prediction
models, the cubic function model could predict the chlorophyll
concentration in the middle and lower reaches of the Hanjiang River most
realistically. Therefore, this study chose the cubic model as the
inversion model of the wave band ratio, and the calculation formula is
as follows (7):

where y is the prediction value of chlorophyll concentration and x is
B7/(B6-B5).

In this section, 60 remote sensing images were randomly selected from 64
remote sensing images, which were extracted using ENVI5.3, and the band
combination with the highest correlation with the measured chlorophyll-a
concentration was selected as the input factor of the BP neural network
model input layer according to Table 3, namely B7/(B6+B5), B7/B5, B4-B2,
B4/(B3+B2). Whether the BP neural network model can solve the nonlinear
problem accurately depends on the number of hidden layer nodes set in
the neural network model, and number of nodes that directly affects the
mapping ability of the neural network to the nonlinear problem (Chen et
al., 2008). In this study, the trial and error method was used to
calculate the node number of the final hidden layer. The results are
listed in \textbf{Table 5} .

According to \textbf{Table 5} , when the number of nodes is four, the
judgment coefficient R\textsuperscript{2} is 0.98918, and the RMSE is
3.1462. The principle of the neural network model shows that the more
hidden layer nodes, the more complex the model, and the longer the
required training time. Therefore, the number of hidden layer nodes was
determined to be four.

{[}Insert Table 5. here{]}

Finally, four waveband combinations of B7/(B6-B5), B7/B5, B4-B2, and
B4/(B3 + B2) were used as input factors for the BP neural network. The
hidden layer was identified as a single layer, and the node number of
the hidden layer was set to four according to \textbf{Table 3} . The
hidden layer transfer function was an S-shaped hyperbolic tangent
function, and the output layer was the measured chlorophyll-a
concentration value synchronized with the waveband combination. The
maximum training time was set to 1000, and the learning rate was set to
0.001 of 60 actual samples, 55 samples were selected randomly for
training, and the remaining five samples were used as test samples.

\textbf{{[}Insert Fig5. here{]}}

As shown in \textbf{Fig. 5} , when using the BP neural network model,
the judging coefficient R\textsuperscript{2} is 0.98918, the RMSE is
3.1462, the RE is 29.1\%, the minimum is 8\%, the average RE is 21.4\%,
and the simulation forecasting effect is poor. Therefore, the BP neural
network model predicted less error than the band ratio model, indicating
that the inversion of chlorophyll-a concentrations in the middle and
lower reaches of the Hanjiang River was more accurate using the BP
neural network model. Additionally, the study confirmed that the BP
neural network model was more suitable for simulating nonlinear problems
arising from the complex optical properties in water bodies.

\subsection*{3.3 Spatiotemporal variation analysis of typical annual
chlorophyll-a
concentration}

{\label{spatiotemporal-variation-analysis-of-typical-annual-chlorophyll-a-concentration}}

The investigation shows that the eruption of water blooms in 2000 and
2008 lasted from late February to mid-March and from late January to
early March, respectively. In this study, the chlorophyll-a
concentration in the middle and lower reaches of the Hanjiang River
during the bloom eruption period in 2000 and 2008 was inverted from the
remote sensing image data of MODIS on February 24, March 4, and March 12
of 2000 and January 2, January 17, February 3, February 14, March 7, and
March 14 of 2008 and the trend in the bloom area was analyzed. When the
concentration of chlorophyll-a was 0--10 \selectlanguage{greek}μ\selectlanguage{english}g/L, the water quality was
better. When the concentration of chlorophyll-a was 10--25 \selectlanguage{greek}μ\selectlanguage{english}g/L, there
was no obvious bloom; when the concentration of chlorophyll-a was 25--35
\selectlanguage{greek}μ\selectlanguage{english}g/L, there was a bloom; and when the concentration of chlorophyll-a was
\textgreater{} 35 \selectlanguage{greek}μ\selectlanguage{english}g/L, there was a severe bloom.

From \textbf{Fig. 6} , it can be seen that on February 24, 2000, the
total concentration of chlorophyll-a in the middle and lower reaches of
the Hanjiang River was relatively high, the concentration of
chlorophyll-a in the area near the monitoring section of the Yuekou
River was \textgreater{} 25 \selectlanguage{greek}μ\selectlanguage{english}g/L in a wide banded distribution, and the
concentration of chlorophyll-a in the area \textless{} 10 \selectlanguage{greek}μ\selectlanguage{english}g/L was
mainly distributed in the area near Zhongxiang City and the lower
reaches of Shayang County. On March 4, 2000, the overall chlorophyll-a
concentration in the middle and lower reaches of the Hanjiang River
continued to increase, and the areas with chlorophyll-a concentrations
\textgreater{} 35 \selectlanguage{greek}μ\selectlanguage{english}g/L were distributed throughout the study area and
were more widely distributed in the upper reaches of Xiantao City and
near Shayang County, showing a band-like distribution, while the rest
were scattered along both riverbanks. Compared with February 24 and
March 4, the water bloom outbreak area increased significantly,
spreading from Zhongxiang to Shayang, Yuekou to Xiantao, and gradually
spreading to the center of the river channel from the shore. The
concentration of chlorophyll-a was \textgreater{} 35 \selectlanguage{greek}μ\selectlanguage{english}g/L in the lower
reaches of Shayang County and the upper reaches of Yuekou Town, and it
was distributed in blocks along the west bank. Chlorophyll-a
concentration decreased downstream of Yuekou Town and upstream of
Xiantao City, and the water bloom phenomenon improved and the water
bloom migrated to the vicinity of Shayang County.

{[}Insert Fig6. here{]}

\textbf{Fig. 7} shows that from January 2 to January 17 in 2008, the
overall chlorophyll-a concentration in the study area was not high. On
the 3rd, the concentration of chlorophyll-a in the downstream water body
area of Zhongxiang City was relatively high. The 10 \selectlanguage{greek}μ\selectlanguage{english}g/L to 25 \selectlanguage{greek}μ\selectlanguage{english}g/L
chlorophyll-a concentration area was distributed in a band from
Zhongxiang City to Shayang County. On February 14, 2008, the entire
middle and lower reaches of the Hanjiang River, the lower reaches of
Zhongxiang City, and some waters near Yuekou Town had chlorophyll-a
concentrations \textless{} 10 \selectlanguage{greek}μ\selectlanguage{english}g/L. Near Shayang County, the
chlorophyll-a concentrations were higher. If concentrations are
\textgreater{} 35 \selectlanguage{greek}μ\selectlanguage{english}g/L, it indicates that the water body is highly
eutrophic, and serious blooms are likely. On March 7, 2008, the
concentration of chlorophyll-a in the study area was \textgreater{} 35
\selectlanguage{greek}μ\selectlanguage{english}g/L, and was more evenly distributed throughout the study area, mostly
concentrated on the shore. The areas where the chlorophyll-a
concentration was \textgreater{} 25 \selectlanguage{greek}μ\selectlanguage{english}g/L near the upper reaches of
Zhongxiang City and Shayang County were denser, and water blooms
appeared from Zhongxiang City and Shayang County. On March 14, 2008, the
area of chlorophyll-a concentration \textless{} 10 \selectlanguage{greek}μ\selectlanguage{english}g/L began to
gradually increase, the areas with chlorophyll-a concentration
\textgreater{} 35 \selectlanguage{greek}μ\selectlanguage{english}g/L also decreased, and the water bloom showed an
overall dissipating trend.

{[}Insert Fig7. here{]}

\subsection*{3.4 Analysis of interannual variation in chlorophyll-a
concentration distribution
area}

{\label{analysis-of-interannual-variation-in-chlorophyll-a-concentration-distribution-area}}

A preliminary investigation found that water blooms occur in the middle
and lower reaches of the Hanjiang River in February and March each year
(Yang et al., 2012; Zhu et al., 2008). To study the variation in
chlorophyll-a concentration in the middle and lower reaches of the
Hanjiang River during the bloom period, this section reverses the
chlorophyll-a concentration distribution during the bloom period from
2000 to 2011 (from February to March). As shown in \textbf{Fig. 8} , the
chlorophyll-a concentration distribution accounts for the results
in\textbf{Fig. 9} .

As can be seen from \textbf{Fig. 8} and \textbf{Fig. 9} , the areas with
chlorophyll-a concentrations \textless{} 10 \selectlanguage{greek}μ\selectlanguage{english}g/L were relatively high in
2001, 2004, 2005, and 2009, accounting for 78.3\%, 82.4\%, 64.3\%, and
75.8\%, respectively, indicating that the chlorophyll-a concentration in
most waters remained low at this time, the water quality was generally
good, and the risk of bloom outbreaks was relatively low. The trend line
in \textbf{Fig. 9 (1)} shows that the area ratio with chlorophyll-a
concentration \textless{} 10 \selectlanguage{greek}μ\selectlanguage{english}g/L fluctuated between 2000 and 2011, but
there was a slight downward trend overall, indicating that the water
quality in the middle and lower reaches of the Hanjiang River gradually
improved during this period. In 2000, 2006, 2007, and 2008, the
concentrations of chlorophyll-a \textgreater{} 25 \selectlanguage{greek}μ\selectlanguage{english}g/L were 57.9\%,
70.2\%, 49.3\%, and 50.10\%, respectively, indicating that the
concentration of chlorophyll-a was high, eutrophication was high, water
quality was poor, and water bloom outbreaks occurred easily or had
already occurred. The area ratio of chlorophyll-a \textgreater{} 25 \selectlanguage{greek}μ\selectlanguage{english}g/L
remained stable and low from 2001 to 2005, and remained stable but high
from 2006 to 2008, compared with the area ratio of chlorophyll-a
\textless{} 10 \selectlanguage{greek}μ\selectlanguage{english}g/L. According to the trend line, the chlorophyll-a
concentration area ratio remained stable from 2000 to 2011 during the
bloom-prone period.

{[}Insert Fig8. here{]}

{[}Insert Fig9. here{]}

\subsection*{3.5 Interannual variation analysis of chlorophyll-a
concentration
value}

{\label{interannual-variation-analysis-of-chlorophyll-a-concentration-value}}

To better reflect the interannual variation in chlorophyll-a
concentration values, this section inverts the chlorophyll-a
concentration values of five monitoring sections (Huangzhuang, Shayang,
Yuekou, Xiantao, and Zongguan) between 2000 and 2011, and analyzes the
interannual variation based on the inversion results. The results are
shown in \textbf{Fig. 10} , and the statistical results are presented
in\textbf{Table 6} .

{[}Insert Fig10. here{]}

{[}Insert Table 6. here{]}

According to the statistical results, during the twelve years from 2000
to 2011, the highest chlorophyll-a concentration was 41.837 \selectlanguage{greek}μ\selectlanguage{english}g/L in
February 2008 at the Shayang monitoring section, and the lowest
chlorophyll-a concentration was 2.774 \selectlanguage{greek}μ\selectlanguage{english}g/L in October 2001 at the
Huangzhuang monitoring section. From the average chlorophyll-a
concentrations in each of the twelve years, the chlorophyll-a
concentrations in 2000, 2006, and 2008 were higher than those in other
years, which is consistent with the historical data showing the years
with water blooms, and the chlorophyll-a concentrations gradually
increased. From the average chlorophyll-a concentrations in each of the
twelve years, the chlorophyll-a concentrations were higher in 2000,
2006, and 2008 than in other years, which is consistent with the
historical data showing the years with water bloom outbreaks.
Furthermore, the chlorophyll-a concentration gradually increased between
2001 and 2004, indicating that the algae in the middle and lower reaches
of the Hanjiang River increased during this period, the eutrophication
of the water body increased, and the water quality deteriorated. Among
the statistical data, chlorophyll-a concentrations exceeding 30 \selectlanguage{greek}μ\selectlanguage{english}g/L
occurred fourteen times, including three times in 2000, four times in
2006, and three times in 2008, and less frequently in the remaining
years. Based on the analysis of the interannual chlorophyll-a
concentration values at each monitoring section, the monthly average
chlorophyll-a concentration values \textgreater{} 30 \selectlanguage{greek}μ\selectlanguage{english}g/L at
Huangzhuang, Shayang, Yuekou, Xiantao, and Zongguan monitoring sections
were three, five, two, four, and two times from 2000 to 2011,
respectively, indicating that the probability of occurrence of water
bloom in the Shayang and Xiantao river sections was higher than that in
other river sections, and was predominantly concentrated in spring.

\section*{4. DISCUSSION}

{\label{discussion}}

Algal bloom outbreaks refer to the phenomenon of excessive reproduction
and aggregation of algae formed under certain nutrition, climate,
hydrological conditions, and ecological environments (Le et al., 2017).
The outbreak of algal blooms is an ecological problem that depends not
only on the nutrient conditions of algal growth, but also on the
ecological environment in which the algae grow. Meanwhile, algal blooms
also depend on the relationship between algae and other aquatic
organisms and the ecological environment, such as changes in
hydrological and meteorological conditions that affect these ecological
relationships. In this section, the effects of environmental factors on
algal blooms are discussed by analyzing the effects of environmental
factors on chlorophyll-a concentration.

\subsection*{4.1 Effect of water temperature on concentration of
chlorophyll-a}

{\label{effect-of-water-temperature-on-concentration-of-chlorophyll-a}}

Most studies show that most water blooms in the Hanjiang River are
diatom blooms (Xu et al., 2005). When the temperature was \textgreater{}
9 \selectlanguage{ngerman}°C, the growth of diatoms became active, and grew well between 15 °C
and 35 °C, but the optimal temperature range was 10 °C to 17 °C, which
was beneficial to the cell division and pigment accumulation of
diatomaceous bloom algae (Zheng, 2005); therefore, the concentration of
chlorophyll-a, an indicator of algae, was higher. Taking Xiantao as an
example, from February 2000 to March 2008, the average measured water
temperatures were 11.5 °C and 12 °C, respectively, but no bloom occurred
in 2009 and 2010, at 9 °C and 10 °C, respectively. In contrast, the
water temperature in the bloom eruption period was approximately 2 °C
higher. Water temperature is one of the factors affecting chlorophyll-a
concentration.

\subsection*{4.2 Effect of hydrological conditions on chlorophyll-a
concentration}

{\label{effect-of-hydrological-conditions-on-chlorophyll-a-concentration}}

Hydrological conditions include water level, discharge, and velocity;
when blooms occur, the water level, discharge, and velocity of the
middle and lower reaches of the Hanjiang River are significantly lower
than those of the same period in previous years (Wang et al., 2004).
Bloom outbreaks in the Hanjiang River generally occur in the early
spring of February to March each year, which is the Hanjiang River dry
season when the water level is low and has good transparency. At this
time, bloom diatoms can make full use of sunlight for photosynthesis and
accumulate photosynthetic products, thereby facilitating cell division
and proliferation and causing an increase in chlorophyll-a concentration
(Tang et al., 2001). According to the measured data, the average water
level of at the hydrometric station in 2000, 2003, and 2008 was 23.71 m
and the average flow rate was 474.3 m/s, which was 1.67 m lower and the
average flow rate was 55.43\% lower than that in 2001, 2004, and 2005.
The flow rates of the Zongguan sections in 2000, 2003, 2006, and March
2008 were 0.168 m/s, 0.166 m/s, 0.267 m/s, 0.230 m/s, respectively, and
the average flow rate was 0.208 m/s, according to the flow data and the
flow rate calculated from the cross-sectional area of the diatom bloom.
In contrast, the average velocity of the Zongguan section from January
to March was 0.289 m/s, and the average velocity of the Zongguan section
decreased by 28.02\%. As can be seen from \textbf{Fig. 11} at higher
chlorophyll-a concentrations, the discharge is smaller and the water
level is lower. Therefore, the variation in chlorophyll-a concentration
is affected by hydrological factors such as water level, discharge, and
velocity.

{[}Insert Fig.11. here{]}

{[}Insert Table 7. here{]}

{[}Insert Table 8. here{]}

\subsection*{4.3 Effect of nutrient on chlorophyll-a
concentration}

{\label{effect-of-nutrient-on-chlorophyll-a-concentration}}

Nitrogen and phosphorus are the main nutrients required for
phytoplankton reproduction and growth (Lin et al., 2021; Liu et al.,
2021). Because of the large variation in chlorophyll-a concentration
values in the middle and lower reaches of the Hanjiang River from 2003
to 2009, the chlorophyll-a concentration values from the inverse
performance of the Zongguan monitoring section in this section were
compared with the measured total nitrogen and total phosphorus values,
and the results are shown in \textbf{Fig. 12} . It was found that the
variation in chlorophyll-a concentration was similar to that of nitrogen
and phosphorus in all years except 2005, that is, the increase in
chlorophyll-a concentration values was accompanied by an increase in
nitrogen and phosphorus concentrations. This shows that the nitrogen and
phosphorus content in the water column during the water bloom could meet
the needs of the proliferation of diatoms in the Hanjiang River water
bloom, and with the increase in nitrogen and phosphorus concentrations,
the diatom growth trend improved, which increased the chlorophyll-a
concentration in the water column. However, numerous studies (Luo et
al., 2021; Jing et al., 2019; Guenther et al., 2015) have also shown
that high concentrations of nitrogen or phosphorus may lead to
uncoordinated nitrogen--phosphorus ratios in the water column, thus
affecting the growth of diatoms in the water. Therefore, the variation
in nitrogen and phosphorus nutrient concentrations and the ratio of
nitrogen and phosphorus nutrients are the main factors affecting the
variation in chlorophyll-a concentration.

{[}Insert Fig.12. here{]}

\section*{5. CONCLUSIONS}

{\label{conclusions}}

Based on the results of the correlation analysis, the BP neural network
model was used to invert the chlorophyll-a concentration in the middle
and lower reaches of the Hanjiang River in 2000--2011. The typical
annual chlorophyll-a concentration distribution map of bloom outbreaks
in the middle and lower reaches of the Hanjiang River was used to
analyze the bloom outbreak process. According to the calculation results
of the percentage of chlorophyll-a concentration distribution in the
middle and lower reaches of the Hanjiang River during the bloom period
from 2000 to 2011, the area with chlorophyll-a concentration \textless{}
10 \selectlanguage{greek}μ\selectlanguage{english}g/L was relatively high in 2001, 2004, 2005, and 2009, the
concentration of chlorophyll-a in most waters was relatively low, and
the water quality was relatively good. The area with chlorophyll-a
concentration \textgreater{} 25 \selectlanguage{greek}μ\selectlanguage{english}g/L was relatively high in 2000, 2006,
2007, and 2008, and the concentration of chlorophyll-a in most waters
was relatively high, eutrophication was relatively high, and water
quality was generally poor. By analyzing the influence of environmental
factors on the concentration of chlorophyll-a, the water temperature and
hydrological conditions, including water level, flow rate, and
concentration and ratio of nutrients, including nitrogen and phosphorus,
were established as the main factors leading to an increase in
chlorophyll-a concentration in the middle and lower reaches of the
Hanjiang River.

\section*{ACKNOWLEDGEMENTS}

{\label{acknowledgements}}

Funding: This work was supported by the National Natural Science
Foundation of China {[}grant numbers 51879239, 51879252{]}, and the
Program for key Science \& Technology projects in Universities of Henan
Province {[}No. 21A570008{]}.

\section*{DATA AVAILABILITY}

{\label{data-availability}}

Data is not shared publicly

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\textbf{Hosted file}

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