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\begin{document}

\title{An evaluation and regulation method for stereoscopic spatial
connectivity of a wetland system based on hydrological change: A case
study of the Heilongjiang River Basin in China}



\author[1]{Qin Yang}%
\author[2]{Peng Hu}%
\author[3]{Jianhua Wang}%
\author[3]{Qinghui Zeng}%
\author[2]{zefan yang}%
\author[3]{Huan Liu}%
\author[1]{Yiyang Dong}%
\affil[1]{Tsinghua University}%
\affil[2]{IWHR}%
\affil[3]{China Institute of Water Resources and Hydropower Research}%


\vspace{-1em}



  
  \date{May 14, 2024}


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\selectlanguage{english}
\begin{abstract}
The weakened connectivity of wetland systems is the key factor leading
to the destruction, degradation, and disappearance of wetlands. The
study of the change of wetland system connectivity enables understanding
the hydrological process in wetland system and providing significant
support for the study of ecological water demand. However, research on
the connectivity of wetland systems has primarily focused on the
intuitive connectivity in terms of hydrology and geomorphology in recent
years, while the impact of wetland systems on habitat has been ignored.
In this study, an innovative method was applied to evaluate and regulate
the stereoscopic spatial connectivity (SSC) of the wetland system in the
Heilongjiang River Basin in China (HRBC). In this method, the water
requirements of typical organisms in the region were considered, and the
hydrological trend in the wetland system as well as the health
conditions of the SSC were analyzed using remote sensing image. A
regulation mode for improving the stereoscopic spatial connectivity
index (SSCI) was proposed. The results revealed that over the past 35
years, the wetland system in the study area shrank significantly, with
the SSCI decreasing from 41.30\% in 1980 to 35.08\% in 2015. By
comparing the correlation among temperature, precipitation, agricultural
land, construction land, and the wetland system during the same period,
it was proven that human activity is the major driving force behind the
observed wetland system shrinkage. Subsequently, the key protected areas
required to maintain the SSC of the wetland system were clarified, and
the key recovery areas were determined according to the three scenarios
of `high--medium--low' feasibility, which greatly improved the SSCI and
generalization route (GR) after regulation. In general, the proposed SSC
evaluation methods can fully reflect the ecohydrological process of
wetland systems. The methods also scientifically quantify the
significant effects of the regulation mode, which has certain relevance
for the evaluation and regulation of wetland systems in other regions.%
\end{abstract}%



\sloppy


\par\null

\subsection*{1. Introduction}

{\label{614273}}

\emph{Abbreviations}: CA, connected area; DEM, digital elevation model;
EWS, effective water surface; GIS, geographic information system; GR,
generalization route; HA, habitat area; HRBC, Heilongjiang River Basin
in China; KRA, key recovery area; MA, migration area; RCA, restored
connected area; SRTM, Shuttle Radar Topography Mission; SSC,
stereoscopic spatial connectivity; SSCI, stereoscopic spatial
connectivity index.

The wetland is one of the most important natural systems. Wetlands,
along with forests and oceans, constitute the world's three major
ecosystems (Costanza et al., 1997). The wetland system is an important
part of the natural ecological environment. It plays an important role
in conserving water sources, purifying water quality, regulating storage
and drought resistance, maintaining biodiversity, etc., especially in
providing important ecological service functions in the hydrological
process, all of which support the sustainable development of human
economic society and living environments (Zhang et al., 2018). Under the
combined effects of global climate change and human activity, however,
the water cycle in the river basin and its associated physical,
chemical, and biological processes have undergone profound changes,
leading to changes in the hydrology of wetlands, water resource
shortages, water quality deterioration, area shrinkage, and functional
degradation (Acreman et al., 2007; Dong and Zhang, 2013; Zedler, 2003;
Zedler and Kercher, 2005; Zhang et al., 2008). According to the
Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem
Services (IPBES), 87\% of the world's wetlands have been lost over the
past 300 years and 54\% since 1900, making it one of the most severely
damaged ecosystems in the world. Since the 1950s, the loss of wetlands
in tropical and subtropical regions, where agriculture is the main
cause, has accelerated. In Asia, approximately 5,000
km\textsuperscript{2} of wetland area disappears every year due to human
activities, such as dam construction and agriculture, and up to 50\% of
the remaining wetlands are threatened and degraded (Acreman et al.,
2007). The results of the Second China Wetland Data Survey (2009--2013)
revealed that the total wetland area in China is 5.4 \selectlanguage{ngerman}×
10\textsuperscript{7} hm\textsuperscript{2}, and the overall proportion
of wetlands in China during this period was 5.6\%, which was less than
the world average of 8.6\%. The total area of natural wetlands is 46.67
million hm\textsuperscript{2}, accounting for 87.1\% of the total
wetland area. Compared with the first survey in 2003, the area of
natural wetlands decreased by 3.38 million hm\textsuperscript{2}, a loss
rate of 9.3\%. In addition, approximately 69.3\% of wetlands are
threatened by overgrazing, pollution, and reclamation, threats that
exert a great impact on the protection of the ecological environment and
the development of economy and society (Niu et al., 2012). Therefore,
the wetland is an important strategic ecological resource. The
destruction, degradation, and disappearance of wetland systems have
caused major problems in the aquatic ecology and hydrological processes
of wetlands.

Most of the transfer processes of material, energy, and information flow
in wetland systems are related to connectivity. The weakened
connectivity of wetland systems is the key factor leading to the
destruction, degradation, and disappearance of wetlands. In the usual
sense, connectivity includes 3 facets: geomorphology, hydrology, and
biological processes. The connectivity of geomorphic systems is defined
as the efficiency of material transfer between system components (Wohl
et al., 2019), where the material can be water, sediment, nutrients, and
so on, and the system components can be rivers, wetlands, or watersheds.
The connectivity of hydrological systems refers to the connectivity of
water transfer between components of the system. The hydrological
connectivity of wetlands determines their soil type, water chemistry,
and vegetation structure (Cook and Hauer, 2007), which governs their
ecology and persistence (Ferone and Devito, 2004). Previous research on
the hydrological connectivity of wetlands has focused on the
interconnections between wetlands and adjacent water bodies, such as
rivers, streams, and other wetlands, in order to grasp the temporal and
spatial (dynamic) connection structure of wetlands relative to their
watersheds (Ameli and Creed, 2017; Evenson et al., 2015; Jaramillo et
al., 2018; Karim et al., 2012; Karim et al., 2014; Karim et al., 2016;
Leibowitz and Vining, 2003; McDonough et al., 2015). Some researchers
have also proposed the vertical connectivity, horizontal connectivity,
and temporal connectivity of wetland water systems. They utilized a
variety of indicators to comprehensively quantify the connectivity of
the systems, providing a basis for identifying areas of aquatic biota
that are vulnerable to interference using appropriate scenario models
(Januchowski-Hartley et al., 2013; Poff and Zimmerman, 2010; Richter et
al., 1996; Rivers-Moore et al., 2016). This brief literature overview
indicates that the current research on wetland connectivity has
primarily focused on two-dimensional plane scale connectivity. For a
large number of migratory birds that are important protection targets of
wetlands, however, their requirements for wetland connectivity are
better reflected in the process of habitation and migration. For
example, an appropriate distance between wetlands is required to meet
the needs of short stays and rest intervals during the flight of
migratory birds and for flying from one habitat to another. This kind of
connectivity requirement breaks through the two-dimensional plane
characteristics of the existing wetland connectivity evaluation, and is
referred to as the stereoscopic spatial connectivity (SSC) of wetlands
in this manuscript.

This study had the following objectives: (1) Establish an innovative
theoretical framework for the evaluation and regulation of the SSC of
wetland systems and propose a calculation method for the stereoscopic
spatial connectivity index (SSCI). (2) Interpret the land use types in
the Heilongjiang River Basin in China (HRBC) using remote sensing, and
screen the wetland systems suitable for the habitat of typical migratory
birds (i.e., red-crowned cranes). Then analyze and discuss the
evolutionary trend of the wetland system and explore its important
driving factors. (3) Calculate the SSCI of typical years and evaluate
the SSC in wetland systems. (4) Develop a multi-scenario regulation
scheme for the comparative analysis of the connectivity effect after
regulation. The method of evaluating and regulating the SSC of wetland
systems proposed in this study not only provides valuable support for
the protection, restoration, and sustainable management of the wetland
system in the HRBC, but also has important significance for other
wetland systems with typical migratory bird protection requirements.

\subsection*{2. Study area and datasets}

{\label{study-area-and-datasets}}

\subsubsection*{\texorpdfstring{\emph{2.1. Study
area}}{2.1. Study area}}

{\label{study-area}}

\textbf{Fig. 1} . (a) Map of study area and (b) Red-crowned crane's
migration route.

The Heilongjiang River (also known as the Amur) is the largest river in
Northeast Asia. It is located between 47°40\selectlanguage{english}'--53deg34\selectlanguage{english}'N and
121deg28\selectlanguage{english}'--141deg20\selectlanguage{english}'E. Its river basin spans China, Mongolia, and Russia,
with an area of more than 1.8 million km\textsuperscript{2}. The study
area of this investigation was the Heilongjiang Basin in China (Fig.
1a). In this river basin, the Heilongjiang is the main stream, while the
main tributaries include the Songhua River, the Wusuli River, and the
Nenjiang River. The length of Heilongjiang River in China is
approximately 2,320 km, the watershed area is 0.91 million
km\textsuperscript{2}, and the areas of the mountains and the plains are
almost equal. The lowest monthly average temperature in the area is
below \selectlanguage{english}-30degC in January, while the highest is 16degC--18degC in July. The
annual average precipitation is 579.1 mm. Within the study area, the
Daxing'an Mountains are situated to the west, the Changbai Mountains to
the east, and the Xiaoxing'an Mountains to the north. The valley is
surrounded by mountains on three sides, and contains the Sanjiang Plain
and Songnen Plain.

The HRBC is located in humid, semi-humid, and semi-arid climate zones.
There are numerous types of wetlands in this basin, including 12 types
of freshwater wetlands. The rivers in the study area are mostly plain
rivers with flat terrain, which is not conducive to drainage. Therefore,
the rivers overflow into swamp wetlands consisting of scattered and
diffuse marshes, accounting for 66.2\% of the total wetland area. Based
on the characteristics of wetland distribution, the wetland can be
divided into five areas: the Daxing'anling Wetland, the Xiaoxing'anling
Wetland, the Changbai Mountain Wetland, the Sanjiang Plain Wetland, and
the Songnen Plain Wetland. The distribution of wetlands in other
locations tends to be scattered. Due to its extremely rich wetland
resources, the HRBC has become an important and key breeding ground and
migration stop for cranes. There are 15 kinds of cranes in the world,
nine of which can be found in China, among which seven are distributed
in the study area. Among these, red-crowned cranes, which are protected
as first-class national protected animals, are also widely distributed
in the HRBC (Fig. 1b). The population of wild red-crowned cranes in the
HRBC is about 280, accounting for more than 10\% of the global wild
red-crowned cranes. They mainly breed in the Zhalong National Nature
Reserve, Xianghai National Nature Reserve, Momoge Nature Reserve,
Xingkai Lake Nature Reserve, and many other national nature reserves (Ma
and Li, 1990; Xu et al., 1995).

With the development of the social economy in the HRBC, the wetland area
has been sharply reduced and the biodiversity has been damaged, bringing
great challenges to the survival environment of red-crowned cranes. For
example, the Zhalong National Nature Reserve, which is one of the
important habitats of red-crowned cranes, has seen its wetland area
shrink in the past few decades, with a corresponding decrease in the
number of wild red-crowned cranes (Wang et al., 2011; Wang et al., 2017;
Yu and Liu, 2018). Meanwhile, in the Sanjiang Plain Wetland, the
agricultural land area has increased by nearly 4-fold over the past 60
years, while the wetland area has decreased from 4.43 million
hm\textsuperscript{2} to 1.51 million hm\textsuperscript{2}, a loss rate
of 0.058 million hm\textsuperscript{2} per year (Yi, 2014). The
fragmentation of the wetland system has resulted in some wetlands no
longer featuring the conditions necessary for red-crowned crane
habitation, which has greatly reduced the landscape connectivity of the
habitat and seriously affected the spatial distribution of the
red-crowned crane (Ma and Jin, 1985; Zhao et al., 2019). Therefore,
wetland protection in the HRBC faces numerous difficulties and problems.
Wetland protection is no longer limited to the maintenance of the status
quo, but now must focus on the restoration and reconstruction of
degraded and damaged wetland systems.

\subsubsection*{\texorpdfstring{\emph{2.2. Data
sources}}{2.2. Data sources}}

{\label{data-sources}}

The data source of this study is the land use data (30m * 30m) jointly
produced by the Institute of geographic sciences and natural resources
research, Chinese Academy of Sciences and the Resource and Environment
Data Cloud Platform. Based on the interpretation of the acquired Landsat
image data and the verification of field investigation, the land use
remote sensing images of the Heilongjiang river basin in China in 1980,
1990, 2000, 2005, 2010 and 2015 were extracted. In addition,
meteorological data are from the China meteorological science data
sharing network (http://data.cma.cn) and the hydrological yearbook of
the People's Republic of China, which include precipitation, temperature
and so on.

\subsection*{3. Methods}

{\label{methods}}

\subsubsection*{\texorpdfstring{\emph{3.1. Analysis of wetland system
evolution
trend}}{3.1. Analysis of wetland system evolution trend}}

{\label{analysis-of-wetland-system-evolution-trend}}

In this study, based on the migration and habitat conditions of typical
migratory birds (i.e., red-crowned cranes), five land use types were
selected to constitute the wetland system, namely, river, lake,
reservoir, beach, and marsh. Based on the classification and statistics
function of the ArcGIS software package, the landscape area change,
landscape dynamicity model, and landscape conversion matrix of the
wetland system were analyzed and calculated.

\emph{3.1.1. Landscape area change}

{\label{landscape-area-change}}

According to the interpreted landscape pattern type data, the areas of
different landscape types in any 2 periods were counted and the change
of landscape area was calculated. The expression used was:

\(U=U_{b}-U_{a}\) (1)

where \(U\) denotes change in landscape area,
km\textsuperscript{2};\(U_{a}\) represents the area of some
type of landscape in the initial stage, km\textsuperscript{2}; and
\(U_{b}\) represents the area of some type of landscape in the
last stage, km\textsuperscript{2}.

\emph{3.1.2. Landscape dynamicity model}

{\label{landscape-dynamicity-model}}

The dynamic rate of the wetland landscape and the dynamic rate of the
integrated wetland landscape were important indicators when evaluating
the dynamic change rate of certain landscape types and regional
landscapes during the study period. The formulas for both indicators are
as follows (Bai et al., 2013; Ouyang et al., 2018):

\(LC=\frac{U_{b}-U_{a}}{U_{a}}\times\frac{1}{T}\times 100\%\) (2)

\(LCG=\frac{\sum_{i=1}^{n}{LU_{i-j}}}{2\sum_{i=1}^{n}{LU_{i}}}\times\frac{1}{T}\times 100\%\)(3)

where LC denotes the dynamic rate of a certain wetland
landscape type; \(LU_{i}\) is the landscape area at the initial
stage;\(LU_{i-j}\) is the absolute value of the area when the
landscape is converted into other landscape types during the research
stage; and\(T\) is the length of the research stage. If
\(T\) represents time in years, LC and
LCG denote the annual dynamic rates of a particular
wetland landscape type and of the entire wetland landscape,
respectively.

\emph{3.1.3. Landscape conversion matrix}

{\label{landscape-conversion-matrix}}

The conversion matrix has been widely adapted to study landscape type
change. Conversion matrices can comprehensively and specifically
characterize the direction and structural characteristics of various
types of landscape changes, and can also be utilized to precisely study
the loss and transfer between two types of landscape types in a given
area. The conversion matrix can be expressed as follows (Bai et al.,
2013; Li and Zeng, 2018):

\(S_{\text{ij}}=\left|\par
\begin{matrix}S_{11}&amp;S_{12}&amp;\cdots&amp;S_{1n}\\
S_{21}&amp;S_{22}&amp;\cdots&amp;S_{2n}\\
\vdots&amp;\vdots&amp;\vdots&amp;\vdots\\
S_{n1}&amp;S_{n2}&amp;\cdots&amp;S_{\text{nn}}\\
\end{matrix}\right|\) (4)

where \(S\) represents the area of a certain landscape
type; \(n\)refers to the wetland landscape type number;
and \(i\) and \(j\) are the landscape types
in different periods. The landscape conversion matrix can be employed to
obtain information concerning the transformation between different
landscape types at the beginning and the end of the study period, which
has rich statistical significance.

\subsubsection*{\texorpdfstring{\emph{3.2. Evaluation and regulation of
the
SSC}}{3.2. Evaluation and regulation of the SSC}}

{\label{evaluation-and-regulation-of-the-ssc}}

The SSC of the wetland system combines the traditional hydrological
connectivity with the environmental and ecological functions required to
maintain the habitat and migration of migratory birds. The ecological
behavior of typical migratory birds, such as their habitat and
migration, is analyzed and calculated based on the need for internal
wetland functions and wetland connectivity. The SSC fully reflects the
health of the entire large-scale wetland system. Based on the analysis
of the evolutionary trend of the wetland system, the evaluation and
regulation of the wetland system SSC proposed by this research on
migratory bird migration primarily consists of four steps: Step 1.
Remote sensing image processing; Step 2. Screening for effective
habitat; Step 3. Calculation of SSCI; and Step 4. Connectivity
evaluation and regulation (Figs. 2 and 3).

\textbf{Fig. 2} . Flowchart depicting the different methodological steps
of the proposed stereoscopic spatial connectivity (SSC) evaluation and
regulation.

First, the obtained remote sensing image is interpreted. From these
images, the spatial distribution of different land use types in the area
is obtained through interpretation methods (Fig. 3a). Based on the
habits of the typical migratory birds in the study area, the types of
land use suitable for the habitation of these birds are extracted, such
as river, lake, reservoir, beach, and marsh. These are then used as
basic data for the suitable habitat spatial distribution (Fig. 3b).

Second, the effective habitat is screened based on the spatial
distribution of the suitable habitat. Screening for the effective
habitat involves two processes. The first is conducting a preliminary
screening of the spatial distribution map (Fig. 3b) of the suitable
habitat obtained from Step 1. The patches smaller than the minimum area
required for typical migratory birds to inhabit are then removed, in
order to obtain a preliminary screened effective habitat distribution
map (Fig. 3c). The second process divides the entire region into grid
squares of appropriate side lengths, and then projects Fig. 3d onto the
grid. Each grid square is regarded as the minimum unit, and the suitable
habitat area within each grid is calculated cumulatively. The grid
squares containing habitat areas smaller than the minimum required for
typical migratory birds are eliminated, and the effective habitat
distribution map is screened twice (Fig. 3d).

Next, the SSCI of the regional wetland system is calculated. Prior to
the calculation, the distance threshold of the patch connectivity within
the landscape was preset by considering the single flight capability and
migration characteristics of typical migratory birds. The selected
effective habitat distribution map was taken as the center, while the
preset distance threshold was taken as the expansion radius for
effective expansion (Fig. 3e). The ``Euclidean distance'' calculation
method was used in this study, and the distribution formula was:

\({[\left(x_{m}-x_{i}\right)^{2}+\left(y_{n}-y_{j}\right)^{2}]}^{\frac{1}{2}}\times D\leq L\)(5)

In this formula, if the projected mesh in the research area is
\(a*b\),\((x_{m},\ y_{n})\) represents the position
coordinates of the effective water surface mesh particle obtained in
step 3, with\(1\leq m\leq a\),\(1\leq n\leq b\);
(\(x_{i},\ y_{j}\)) represents the position coordinates satisfying the
migration grid of typical migratory birds,
\(1\leq i\leq a\),\(1\leq j\leq b\); \(D\) is the
length of the square grid, km\textsuperscript{2}; and
\(L\) is the maximum flight distance of typical migratory
birds in the target area, km\textsuperscript{2}.

All of the grid squares that meet Eq. (5) are identified and labeled. By
considering factors such as watershed distribution and topography, the
disconnected regions with small distribution ranges are eliminated. The
maximum connected patches meeting the habitat and migration conditions
of the target migratory birds are screened in order to determine the
region of the SSC (Fig. 3f). The ratio of the mesh number of the maximum
connected patches to the total mesh number of the study area represents
the SSC of the regional wetland system. Its expression is:

\(\text{SSCI}=\frac{N_{\text{EWS}}}{N}\times 100\%\) (6)

where \(N_{\text{EWS}}\) (EWS = effective water surface) is the number
of effective grid squares contained in the connected region and
\(N\)represents the total number of grid squares covered
by the target research area.

Finally, based on the calculation results, the optimal control measures
of the SSC of the regional wetland system are proposed. Combined with
the river basin attributes, topography, human activity, and land use
status of the study area, the key protected areas in the connected area
(Fig. 3g) and the key recovery areas in the unconnected area (Fig. 3h)
are proposed under the current conditions, so as to suggest a regional
SSC regulation plan. The connectivity before and after regulation is
compared, and the feasibility of the scheme is comprehensively
considered in order to select and optimize the regulation scheme.

\textbf{Fig. 3} . Operational steps for the evaluation and regulation of
the stereoscopic spatial connectivity (SSC).

\subsection*{4. Results}

{\label{results}}

\subsubsection*{\texorpdfstring{\emph{4.1. Analysis of the wetland system
evolutionary trend in the
HRBC}}{4.1. Analysis of the wetland system evolutionary trend in the HRBC}}

{\label{analysis-of-the-wetland-system-evolutionary-trend-in-the-hrbc}}

The wetland system changes in the HRBC are shown in Fig. 4 and Table 1.
From 1980--2015, the wetland system area decreased from 68,617
km\textsuperscript{2} to 55,468 km\textsuperscript{2}, accounting for
6.1\% of the total area of the study area, down from 7.5\%. Compared
with 1980, the area of the wetland system had decreased by 19.2\% in
2015, indicating a dynamic rate of -0.5\%. Within the wetland system,
the area of marsh exhibited the greatest shrinkage, decreasing 12,114
km\textsuperscript{2}, a change of 25.9\% and a dynamic rate of -0.7\%.
The lake, beach, and river areas all shrank slightly, decreasing 1,088
km\textsuperscript{2}, 399 km\textsuperscript{2}, and 96
km\textsuperscript{2}, respectively. Contrary to these results, the
reservoir area displayed continuous growth. From 1980--2015, the
reservoir area increased by 549 km\textsuperscript{2}, an expansion of
30.7\%, reflecting the largest dynamic rate of 0.9\%.

In terms of time series, the HRBC wetland system area changed more
dramatically between 1980 and 2000. During this period, which
corresponded to the early stage of China's reform and opening-up, human
social activities intensified, and the rate of land reclamation and
urbanization increased rapidly. As a result, marsh, lake, beach, and
river areas all shrank to a certain extent, decreasing 9,713
km\textsuperscript{2}, 705 km\textsuperscript{2}, 207
km\textsuperscript{2}, and 65 km\textsuperscript{2}, respectively. Among
them, the reductions of marsh and lake areas were significant, reaching
-20.8\% and -8\%, respectively, corresponding to dynamic rates of -1\%
and -0.4\%. At the same time, the reservoir area increased by 13.2\%,
although since the reservoir area increased by only 237
km\textsuperscript{2}, its mitigation of the shrinking trend of the
entire wetland system had less impact. The proportion of the wetland
system to the total study area decreased from 7.5\% to 6.4\%. From 2000
to 2015, the shrinkage trend of the HRBC wetland system area became
relatively slow, and the proportion to the total study area decreased
from 6.4\% to 6.1\%. The shrinkage rate was much lower than that of
1980--2000. Thus, the encroachment rate on the wetland system has slowed
down to some extent.

\textbf{Table 1} Area trends of various types of water bodies from
1980--2015.

\textbf{Fig. 4} . Area trends of various types of water bodies from
1980--2015.

Fig. 5 shows the spatial change of the wetland system in the HRBC. It
can be seen that the distribution range of the wetland system decreased
continually from 1980--2015. Within this system, the spatial
distribution of the marsh area decreased most noticeably, primarily in
the Sanjiang Plain. The spatial distribution of the reservoir increased
by a small amount, however, which increased the spatial extent of the
wetland system to a certain extent.

\textbf{Fig. 5} . Spatial distribution trends of various types of water
bodies from 1980--2015.

\subsubsection*{\texorpdfstring{\emph{4.2. Transition matrix analysis of
the wetland
system}}{4.2. Transition matrix analysis of the wetland system}}

{\label{transition-matrix-analysis-of-the-wetland-system}}

The analysis of the conversion among land use types is used to uncover
the internal conversion among different land use types. In this study,
ArcGIS software was utilized to overlay and analyze the spatial
distribution of the wetland system in the HRBC from 1980--2015, thus
obtaining the spatial transition matrices (Tables 2--4, and Fig. 6).
Tables 2 and 3 show the conversions of various types of land use within
the wetland system as well as outside it. As shown in Table 2, from
1980--2015, 17,197 km\textsuperscript{2} of the wetland system was
converted to various types of land use. The conversion loss of marsh was
the largest, reaching 14,949 km\textsuperscript{2} and accounting for
86.9\% of the total conversion loss area, followed by beach, lake,
reservoir, and river areas, with conversion loss areas of 1,047
km\textsuperscript{2}, 958 km\textsuperscript{2}, 155
km\textsuperscript{2}, and 89 km\textsuperscript{2}, respectively. Among
the areas obtained after conversion, agricultural land exhibited the
most generation, reaching 13,476 km\textsuperscript{2} and accounting
for 78.4\% of the total converted area. As shown in Table 3, from
1980--2015, 4,049 km\textsuperscript{2} of various types of external
land use types were converted to the wetland system. Among them, marsh
transferred the most area, reaching 2,865 km\textsuperscript{2} and
accounting for 70.8\% of the total converted area, followed by
reservoir, beach, lake, and river areas (511 km\textsuperscript{2}, 402
km\textsuperscript{2}, 219 km\textsuperscript{2}, and 51
km\textsuperscript{2}, respectively). Among the various types of
external land use transferred to the wetland system mentioned above,
grassland and agricultural land contributed the most, totaling 1,499
km\textsuperscript{2} and 1,341 km\textsuperscript{2}, respectively, and
correspondingly accounting for 37.0\% and 33.1\% of the total
transferred area. Table 4 shows the transfer of various types of land
use within the wetland system from 1980--2015, with the areas of river,
lake, and marsh decreasing, and the areas of reservoir and beach
increasing slightly.

\textbf{Table 2} Reduction of the wetland system area transfer matrix in
2015 compared to 1980.

\textbf{Table 3} Increase of the wetland system area transfer matrix in
2015 compared to 1980.

\textbf{Table 4} Internal transfer matrix of the wetland system area in
2015 compared to 1980.

\textbf{Fig. 6} . Reduced/increased water area distribution in 2015
compared to 1980.

\subsubsection*{\texorpdfstring{\emph{4.3. SSCI evaluation of the wetland
system}}{4.3. SSCI evaluation of the wetland system}}

{\label{ssci-evaluation-of-the-wetland-system}}

In accordance with the temporal variation law of the wetland system in
the study area, this investigation selected 1980, 2000, and 2015 as the
typical years, and chose the red-crowned crane as the target organism
for SSC evaluation. Based on the wetland system distribution map
extracted in Fig. 5, the SSC evaluation method was employed to remove
small patches that did not meet the red-crowned crane's habitat
requirements, and the habitat area grid (5 km \selectlanguage{ngerman}× 5 km) was generalized.
Centering on the grid of the inhabitable areas, and taking the
single-flight capability of a red-crowned crane as a radius of 40 km,
the migratory region was expanded, as shown in Fig. 6a (Li et al., 2015;
Li et al., 1994). The maximum connected patches (Fig. 6b) were then
screened in the migratory region in order to obtain the evaluation
results of the SSC of the regional wetland system.

As shown in Table 5, in terms of time scale, the habitable area of
red-crowned cranes in the HRBC decreased from 166,950
km\textsuperscript{2} to 138,150 km\textsuperscript{2} over the period
1980--2015. Thus, the area decreased by 28,800 km\textsuperscript{2}, a
17.3\% reduction. The migratable area shrank drastically from
1980--2000, accounting for 89.5\% of the reduction for the entire
period. The migration area of the red-crowned cranes in the basin
decreased from 450,575 km\textsuperscript{2} in 1980 to 414,425
km\textsuperscript{2} in 2015, a decrease of 8\%. The largest connected
patch in the study area decreased from 356,400 km\textsuperscript{2} in
1980 to 302,725 km\textsuperscript{2} in 2015, a decrease of 15.1\%.
Finally, it was determined that from 1980--2015, the SSCI of the river
basin decreased from 41.3\% to 35.1\%, a decrease of 15\%. The reduction
trend was mainly concentrated before 2000, accounting for 96.8\% of the
overall decrease.

In terms of spatial scale, the spatial distribution of wetland system in
HRBC from 1980 to 2018 is shown in Fig.7. The area with the most severe
shrinkage of migratory area was concentrated on the Sanjiang Plain. This
was mainly due to the further large-scale development of agricultural
reclamation on the Sanjiang Plain associated with social and economic
development and the increasing demand for grain that began in the 1980s.
The farmland development area is more than 20 million mu (Yi, 2014). In
addition, the Songnen Plain area is the site of frequent human
activities, including cultivation and urbanization, which have also
caused damage to the wetland system in the basin, leading to a certain
amount of wetland area degradation (Liu et al., 2014; Zhang et al.,
2019). As a result of these changes, the suitable habitat areas and
migration paths of red-crowned cranes have been largely lost, and
measures should be taken to strengthen the protection and restoration of
wetlands in the appropriate areas.

\textbf{Fig. 7} . Stereoscopic connected areas of the wetland system.

\textbf{Table 5} Stereoscopic connected areas and their associated
wetland system index.

\subsection*{5. Discussion}

{\label{discussion}}

\subsubsection*{\texorpdfstring{\emph{5.1. Cause of the evolutionary
trends in the wetland system and stereoscopic spatial
connectivity}}{5.1. Cause of the evolutionary trends in the wetland system and stereoscopic spatial connectivity}}

{\label{cause-of-the-evolutionary-trends-in-the-wetland-system-and-stereoscopic-spatial-connectivity}}

The SSC changes described in this study were primarily caused by the
evolution of the wetland system, which was influenced by both climate
change and human activity. Climate change mainly affects the spatial and
temporal distribution of the ecological and hydrological processes in
wetland system areas on a large scale, and is one of the important
driving factors in wetland system change. In particular, temperature and
precipitation are 2 important meteorological factors affecting wetland
systems (Cui et al., 2019; Cui et al., 2018; Leroux et al., 2017; Liu et
al., 2018; Muradyan et al., 2019; Yu et al., 2012). As shown in Fig. 8a
and b, by analyzing the meteorological data of 62 national
meteorological stations located in the HRBC from 1980--2015, the trends
of annual average temperature and precipitation were obtained. The HRBC
has been warming since 1980, with the average annual temperature rising
at a rate of 0.027°C/year. As the temperature rises, evaporation will
increase, and the wetland system will shrink. In addition, the average
annual precipitation in the HRBC has decreased slightly over the past 35
years. Since precipitation is an important water source for wetlands,
the changing precipitation trend will also affect the spatial and
temporal distribution of wetland systems. It is worth noting that from
2000--2015, the average annual precipitation in the study area increased
significantly, and the wetland system shrinkage gradually slowed.

\textbf{Fig. 8} . Trends of various driving factors from 1980--2015.

The influence of human activity, however, remains the most important
factor in wetland system shrinkage in the study area (Chen et al., 2019;
Fluet-Chouinard et al., 2015; Hudon et al., 2018; Zhang et al., 2019).
Fig. 8c and d show that both agricultural land and construction land in
the Heilongjiang Basin are increasing significantly. Of these,
agricultural land increased significantly, at the rate of 1,292.79
km\textsuperscript{2}/year, replacing large sections of the original
wetland system. Moreover, most of the expansion occurred before 2000,
with the increase after 2000 being relatively small, which was basically
consistent with the shrinkage trend of the wetland system. For example,
some researchers discovered that the area of the global wetland
ecosystem exhibited a decreasing trend from 1977--2011 (Costanza et al.,
2014). At the same time, the shrinkage of the wetland system in the HRBC
was mainly due to the change of land use in the Sanjiang Plain area,
where the lost area of the wetland system was primarily converted into
agricultural land, which also led to the decrease of ecosystem service
value (Yan and Zhang, 2019). In addition, improved wetland protection
policy also has an important impact on wetland systems. Since 2000,
increasing attention has been paid to the degradation of wetland
systems, and a large number of national nature reserves have been
improved. The shrinkage trend of the wetland system in the HRBC has been
effectively alleviated by strengthening the protection of wetland
systems (Fu et al., 2015; Liu et al., 2011). In summary, climate change
and human activity have contributed to the shrinking of the wetland
system in the HRBC over the past 35 years, with large-scale agricultural
reclamation and development being the main driving factors behind this
reduction.

\subsubsection*{\texorpdfstring{\emph{5.2. SSC} \emph{regulation scheme
for wetland systems and feasibility
analysis}}{5.2. SSC regulation scheme for wetland systems and feasibility analysis}}

{\label{ssc-regulation-scheme-for-wetland-systems-and-feasibility-analysis}}

In this study, 2015 was selected as the current year and a ``double-key
area'' approach including both key protected areas and key recovery
areas was proposed in order to improve the SSC of the wetland system in
the river basin. The key protected areas are the key areas that should
be protected in order to maintain the existing connected system. Fig. 9a
shows the selected key protected areas. The following 2 factors are
considered during screening: (1) In terms of ecological function, the
key protected areas are the key channels connecting suitable habitats of
red-crowned cranes. They provide an important guarantee for the wetland
system SSC; (2) In terms of the possibility of being affected, these
areas experience frequent human activity and the wetland system is
relatively fragile. In order to ensure that the connectivity under
current conditions is not destroyed, protection efforts should be
enhanced.

The key restoration area is based on the existing connectivity system.
It restores some areas that currently do not meet the red-crowned
crane's habitat requirements, and can greatly improve the overall
connectivity of the river basin wetland system. Fig. 9b--d shows the
selected recovery areas. During the process of screening the key
recovery areas, the following 3 scenarios were developed based on
feasibility, from high to low.

Scenario (a): High feasibility. At the key points that can significantly
increase the connected area, expand the existing small water areas that
do not meet the habitat conditions of the red-crowned crane, so that the
restored waters meet the habitat conditions. The key recovery areas and
recovery results of scenario (a) are shown in Table 6, Fig. 9b, and Fig.
10b. Through the ecological restoration of small water areas, the
connectivity of the Sanjiang Plain can be restored. The connected area
in this region would be close its 1980 coverage, and connected area
could be added to the southeast of the study area. It was calculated
that the SSCI energy would increase to 38.7\%, and the number of
connected paths within the wetland system would increase from 60 under
the current conditions to 70 after restoration.

Scenario (b): Medium feasibility. Based on scenario (a), the key points
of the red-crowned crane migration path would be selected, and the
dryland, woodland, grassland, and other transformable land use types
would be restored to wetlands that meet the red-crowned crane's habitat.
The key recovery areas and recovery results of scenario (b) are shown in
Table 6, Fig. 9c, and Fig. 10c. Through the transformation of land use
types, the SSCI would increase to 40.6\%, basically returning to the
connectivity level of the basin in 1980. The regulation scheme of
scenario (b) would add generalization routes in the study area,
increasing the total number of generalization routes to 116, which is
1.93 times the pre-regulation total.

Scenario (c): Low feasibility. On the basis of scenario (b), the Songhua
River Basin would be connected to the Heilongjiang Mainstream Basin and
the Ergun River Basin through recovery measures, which would greatly
improve the overall connectivity of the wetland system in the study
area. The key recovery areas and recovery results of scenario (c) are
shown in Table 6, Fig. 9d, and Fig. 10d. The SSCI would increase to
47.6\%, which is 1.36 times its 2015 value. The total number of
generalization route would reach 131, which is 2.18 times the
pre-regulation total.

Based on the results of the above three scenarios and the feasibility of
the schemes, the suggestion of this study is to quickly give priority to
scenario (a) and then to scenario (b). Through the regulation methods of
scenarios (a) and (b), the wetland system area and SSCI for the
red-crowned crane habitat would effectively improve, and the conditions
for the habitat and migration of red-crowned cranes would be restored to
1980 levels. After the number of red-crowned cranes in the region has
increased significantly, consider implementing scenario (c) via human
intervention. Through the regulation mode of scenario (c), the habitat
area and migration area of the red-crowned crane could be greatly
expanded.

\textbf{Table 6} Regulated stereoscopic connected areas and their
associated wetland system index.

\textbf{Fig. 9} . Regulated stereoscopic connected areas of the wetland
system.

\textbf{Fig. 10} . Regulated generalized migration routes of the wetland
system.

\subsection*{6. Conclusions}

{\label{conclusions}}

This study summarized the evolution of the wetland system in the
Heilongjiang River Basin over the past 35 years. Through the
establishment of SSC evaluation and regulation methods for the habitat
and migration requirements of typical migratory birds (in this case,
red-crowned cranes), the potential laws of the ecological hydrological
process and SSCI of the wetland system in the study area are revealed.
The results indicate that the wetland system in the HRBC was seriously
destroyed from 1980--2015. The wetland area decreased by 19.2\% within
these 35 years, during which the increase of agriculture land was the
major driving factor leading to the reduction of wetland in the study
area. During the same period, the SSCI of the wetland system in the
study area also gradually decreased, from 41.3\% in 1980 to 35.1\% in
2015, which compressed the habitat area and migration path of
red-crowned cranes. Based on these findings, five key protected areas
under the current connectivity system of the wetland system in the study
area were screened, and three progressive restoration scenarios were
proposed based on feasibility level, which could greatly improve the
SSCI and the number of generalization routes of the wetland system in
the basin.

Aspects of this topic still require further study. For example, in terms
of the requirements of the habitat and migration of migratory birds,
this investigation only considered wetland size as the evaluation
criterion, which is not comprehensive enough. Other factors such as
water depth, vegetation distribution, and distance from human activities
in the region also need to be analyzed in future studies in order to
more accurately evaluate the SSC in regional wetland systems and to
establish comprehensive regulation measures.

\subsection*{Acknowledgements}

{\label{acknowledgements}}

This work was supported by the National Key Research and Development
Program of China {[}grant numbers 2017YFC0404503, 2016YFC0401302{]}; the
National Natural Science Foundation of China {[}grant number
51625904{]}; and the Basic Scientific Research Expense Project of the
China Institute of Water Resources and Hydropower Research {[}grant
number WR0145B522017{]}.

\textbf{Data Availability Statement}

The data that support the findings of this study are available from the
corresponding author upon reasonable request.

\textbf{Conflict of Interest Statement}

Conflict of Interest -- None.

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\textbf{Figure list}

\textbf{Fig. 1} . (a) Map of study area and (b) Red-crowned crane's
migration route.

\textbf{Fig. 2} . Flowchart depicting the different methodological steps
of the proposed stereoscopic spatial connectivity (SSC) evaluation and
regulation.

\textbf{Fig. 3} . Operational steps for the evaluation and regulation of
the stereoscopic spatial connectivity (SSC).

\textbf{Fig. 4} . Area trends of various types of water bodies from
1980--2015.

\textbf{Fig. 5} . Spatial distribution trends of various types of water
bodies from 1980--2015.

\textbf{Fig. 6} . Reduced/increased water area distribution in 2015
compared to 1980.

\textbf{Fig. 7} . Stereoscopic connected areas of the wetland system.

\textbf{Fig. 8} . Trends of various driving factors from 1980--2015.

\textbf{Fig. 9} . Regulated stereoscopic connected areas of the wetland
system.

\textbf{Fig. 10} . Regulated generalized migration routes of the wetland
system.

\textbf{Table list}

\textbf{Table 1} Area trends of various types of water bodies from
1980--2015.

\textbf{Table 2} Reduction of the wetland system area transfer matrix in
2015 compared to 1980.

\textbf{Table 3} Increase of the wetland system area transfer matrix in
2015 compared to 1980.

\textbf{Table 4} Internal transfer matrix of the wetland system area in
2015 compared to 1980.

\textbf{Table 5} Stereoscopic connected areas and their associated
wetland system index.

\textbf{Table 6} Regulated stereoscopic connected areas and their
associated wetland system index.

\textbf{Hosted file}

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