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\begin{document}

\title{Three decades of oasis transition and its driving factors of Turpan-Hami
Basin in Xinjiang, China: a complex network approach}



\author[1]{Qinglan Zhang}%
\author[2]{Min Yan}%
\author[1]{Li Zhang}%
\author[2]{Wei Shao}%
\author[2]{Yiyang Chen}%
\author[2]{Yuqi Dong}%
\affil[1]{Guilin University of Technology College of Geomatics and Geoinformation}%
\affil[2]{Chinese Academy of Sciences}%


\vspace{-1em}



  
  \date{May 16, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Oasis, as a predominant and distinctive resource in arid regions, plays
an important role in maintaining land stability, human production and
living activities. The research on oasis transition dynamics and driving
factors owns vital significance in supporting arid regions sustainable
development. As a typical mountain-desert-oasis landscape, Turpan-Hami
(Tuha) Basin in Xinjiang of China, exhibits sophisticated interactions
among different land types. In this study, we inspected the
spatio-temporal patterns and transition processes of the oasis using a
complex network during 1990 and 2020 in Tuha Basin. In the oasis
transition network, degree value, betweenness centrality, and average
path length were calculated to express the transition relationship, key
oasis type, and oasis structural stability, corresponding. Six factors
were selected to investigate the driving forces for oasis transition
behind climate change and human activities. Our results showed that the
oasis area of Tuha Basin, including natural oasis and artificial oasis,
all grew from 1990 to 2020, with the natural oasis expanding more than
the artificial oasis. The transitions between oasis types became more
frequent as the number of the nodes increased throughout the study
period. Grassland acted as the most important oasis type in the network
with the highest betweenness centrality, but its importance declined due
to the increasing complexity in the oasis transition network from 1990
to 2020. The transitions between oasis types became simpler and the
oasis structural stability were increasingly unstable. Through the
driving analysis, the oasis changes showed positive correlation with
temperature ( \emph{P}-value \textless{} 0.05, \emph{r} = 0.88), and
urbanization and industrialization factors prompted the transitions of
built-up and cropland from grassland and shrubland. Totally, preventing
the degraded grassland and excessive reclamation of land cover,
protecting the shrubland and water resources are suggested in this study
to conduct a harmonious symbiotic relationship between natural
environment and human activities, and promote the oasis sustainable
development.%
\end{abstract}%



\sloppy


\textbf{Three decades of oasis transition and its driving factors of
Turpan-Hami Basin in Xinjiang, China: a complex network approach}

\textbf{Qinglan Zhang\textsuperscript{1,3}, Min
Yan\textsuperscript{2,3}, Li Zhang\textsuperscript{1,2,3}, Wei
Shao\textsuperscript{3,4}, Yiyang Chen\textsuperscript{2,3,5}, Yuqi
Dong\textsuperscript{2,3,5}}

\textsuperscript{1}College of Geomatics and Geoinformation, Guilin
University of Technology, Guilin 541004, Guangxi Province, PR China

\textsuperscript{2}International Research Center of Big Data for
Sustainable Development Goals, Beijing 100094, PR China

\textsuperscript{3}Key Laboratory of Digital Earth Science, Aerospace
Information Research Institute, Chinese Academy of Sciences, Beijing
100094, PR China

\textsuperscript{4}School of Geomatics and Marine Information, Jiangsu
Ocean University, Lianyungang 222001, PR China

\textsuperscript{5}University of Chinese Academy of Sciences, Beijing
100094, PR China

\textbf{Correspondence}

Min Yan, International Research Center of Big Data for Sustainable
Development Goals, Beijing 100094, China.

Email: yanmin@aircas.ac.cn

Li Zhang, International Research Center of Big Data for Sustainable
Development Goals, Beijing 100094, China.

Email: zhangli@aircas.ac.cn

\textbf{Abstract:} Oasis, as a predominant and distinctive resource in
arid regions, plays an important role in maintaining land stability,
human production and living activities. The research on oasis transition
dynamics and driving factors owns vital significance in supporting arid
regions sustainable development. As a typical mountain-desert-oasis
landscape, Turpan-Hami (Tuha) Basin in Xinjiang of China, exhibits
sophisticated interactions among different land types. In this study, we
inspected the spatio-temporal patterns and transition processes of the
oasis using a complex network during 1990 and 2020 in Tuha Basin. In the
oasis transition network, degree value, betweenness centrality, and
average path length were calculated to express the transition
relationship, key oasis type, and oasis structural stability,
corresponding. Six factors were selected to investigate the driving
forces for oasis transition behind climate change and human activities.
Our results showed that the oasis area of Tuha Basin, including natural
oasis and artificial oasis, all grew from 1990 to 2020, with the natural
oasis expanding more than the artificial oasis. The transitions between
oasis types became more frequent as the number of the nodes increased
throughout the study period. Grassland acted as the most important oasis
type in the network with the highest betweenness centrality, but its
importance declined due to the increasing complexity in the oasis
transition network from 1990 to 2020. The transitions between oasis
types became simpler and the oasis structural stability were
increasingly unstable. Through the driving analysis, the oasis changes
showed positive correlation with temperature (\emph{P} -value
\textless{} 0.05, \emph{r} = 0.88), and urbanization and
industrialization factors prompted the transitions of built-up and
cropland from grassland and shrubland. Totally, preventing the degraded
grassland and excessive reclamation of land cover, protecting the
shrubland and water resources are suggested in this study to conduct a
harmonious symbiotic relationship between natural environment and human
activities, and promote the oasis sustainable development.

\textbf{Keywords:} Turpan-Hami Basin, complex network, oasis transition,
oasis structural stability, driving force, oasis sustainable development

\section*{Introduction}

{\label{introduction}}

Oases are the most productive, dynamic, and vulnerable systems in
drylands that supply stable water resource, basic resources and
conditions for human survival and living environments (Bie \& Xie, 2020;
Chen \emph{et al.} , 2022; Xue \emph{et al.} , 2015, 2019). Oases are
also crucial ecological barriers in drylands defeating desertification
(Abuzaid \& Abdelatif, 2022), salinization (Li \emph{et al.} , 2021),
and sandy weather (An \emph{et al.} , 2022). Since the 1950s, artificial
oases had expanded due to the land reclamation and agriculture in
northwest China (Wang, 2010), which contributed to sufficient and stable
food production and economic growth (Zhou \emph{et al.} , 2017), and
caused series of eco-environment problems. Because of the extremely
drought weather and limited water supply, oases had experienced
groundwater reduction, desertification expansion, and grass degradation
(Luo \emph{et al.} , 2020; Ma \emph{et al.} , 2022; Xue \emph{et al.} ,
2019; Yan \emph{et al.} , 2021), which deeply influenced the stability
of oasis systems. Therefore, it is necessary to quantify the oasis
spatio-temporal patterns and its driving factors under natural and human
impacts.

Previous studies have explored the spatio-temporal oasis patterns and
the interactions between oasis and various factors (e.g., natural and
human factors). Land use dynamic model and landscape pattern index were
the common methods to quantify and forecast the oasis dynamic changes
(L\selectlanguage{ngerman}ü \emph{et al.} , 2020; Xiao \emph{et al.} , 2019; Chen \emph{et al.}
, 2023; Liu \emph{et al.} , 2021). For examples, Amuti \& Luo (2014)
analyzed Hotan oasis temporal changes using land use/land cover (LULC)
data and the main driving factors of oasis changes during 1990 and 2008,
which found that intensified human activities reduced the desert-oasis
ecotone. Tan et al. (2020) analyzed the oasis changes of Zhangye city
from 1980 to 2015, and simulated the future oasis changes in 2030
applying future land use simulation model (FLUS), LULC data, geography
and socioeconomic data; the authors found that main transfers of oasis
transition existed in cropland, built up, waterbody and desert from 2015
to 2030. These methods mainly described oasis transitions from a
specific land type to another in terms of type and quantity, ignoring
the changing processes in the entire oasis transition system
(Zhang\emph{et al.} , 2021). Moreover, the identification of key land
type mostly relies on the amount of the changed area and rate, which
could not express the interactions among different oasis types, oasis
and non-oasis types, even the oasis structural stability.

Therefore, the complex network model was introduced in this paper to
analyze the dynamic processes within oasis types. As a theoretical
method, a complex network is composed of a large number of
interconnected nodes, performing substantial non-trivial topological
features of the target system and quantifying the interactions among the
sub-systems (Newman, 2003; Zhang \emph{et al.} , 2020). It has
demonstrated that the complex network is an effective tool in modeling
and analyzing the land use change processes (e.g., urbanization,
afforestation / deforestation) and identifying the key land types during
those processes. Xu et al. (2023) studied the complex relationship in
the procession of LULC using complex network, which concluded that key
land types were more vulnerable to change and had greater impacts on
land ecosystems. Zhang et al\emph{.} (2020) constructed the complex
network of landscape index and patch type index, and evaluated the
stability of landscape and network in Pingshuo opencast mining area in
China during 1986 and 2015. The authors found that the landscape
heterogeneity tended to be unstable, and the network length became
shorter, demonstrating more human activities in mining areas during the
study period. So far, the applications of complex network in
investigating oases transition and stability were scarce, especially on
the long-term large scale.

In this study, we conducted oasis transition processes and structural
stability investigation using the constructed oasis transition network
and analyzed driving factors over Tuha Basin during 1990 and 2020. Our
research aims to (1) find out the spatio-temporal patterns for different
oasis types, (2) simulate the oasis transition processes and identify
the key oasis types, and (3) discover the main driving factors for
natural and artificial oasis.

\section*{Study area and datasets}

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

\subsection*{2.1 Study area}

{\label{study-area}}

Tuha Basin is the abbreviation of Turpan and Hami Basin and located in
eastern Xinjiang of China (86\selectlanguage{english}044'55"-960\selectlanguage{english}25'0"E, 400\selectlanguage{english}49'13"-450\selectlanguage{english}3'21"N,
Figure 1), which comprises two districts (Gaochang and Yizhou) and four
counties (Tuokexun, Shanshan, Balikun, and Yiwu). With the average
elevation of 800 m and elevation drop beyond 5000 m, Tuha Basin shows
high mountain in the north and low basin in the south.

Tuha Basin has uneven distribution of precipitation and temperature.
Rainfall occurs primarily in the mountain areas between April and
September annually. Annual precipitation is usually less than 20 mm,
while evaporation can reach 3000 mm, causing typical arid climate, with
the water resource mostly relying on the snow meltwater from the
mountains. The annual average temperature is around 15 0\selectlanguage{english}C. The maximum
temperature exceeds 500\selectlanguage{english}C in plain areas, whereas in the mountain areas,
it only reaches 27 0\selectlanguage{english}C.

In this study, we categorized oasis into natural and artificial oasis
according to the oasis features and function (Guo \emph{et al.} , 2016;
Wang \emph{et al.} , 2021). Natural oasis was defined as the basic
ecological environment, including forest, shrubland, grassland and
waterbody. Artificial oasis is formed by the combination of natural
disturbances and human interventions, and predominant by cropland and
built-up in Tuha Basin. Other land types (bare land, permanent ice and
snow) were classified into non-oasis type. Generally, oases are situated
in the plain area of Tuha Basin, with the elevation range of --50 to 300
m. There is a small percentage of oases embedded in river basins formed
by the meltwater from snow mountains.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure.1} The geographical location of (a) Xinjiang, (b) Tuha
Basin, and (c) land cover map of Tuha Basin in 2020.

\subsection*{2.2 Datasets}

{\label{datasets}}

Oasis data are extracted from GLC\_FCS30 in Earth System Science Data
(https://doi.org/10.5194/essd-13-2753-2021/), with 30 m spatial
resolution and 5-year interval from 1990 to 2020. GLC\_FCS30 data
classified land types into cropland, forest, shrubland, grassland,
wetlands, built-up, bare land, water body, and permanent ice and snow.
Because of the tiny coverage in Tuha Basin, wetland was merged into
water body. Compared with the apparent vertical zonality of mountainous
areas, oasis owns intrazonal characteristics (Zhang \emph{et al.} ,
2021; Zhou \emph{et al.} , 2010). Therefore, in this study, the areas
with the elevation higher than 2500 m and between 500 m and 2500 m with
the slope greater than 250 \selectlanguage{english}were regarded as mountainous areas and masked
out (Kapos \emph{et al.} , 2000; K\selectlanguage{ngerman}örner \emph{et al.} , 2017, 2021).

Oasis dynamics are mainly affected by natural and human factors
(Chen\emph{et al.} , 2023). Natural factors are characterized by wide
ranges and long duration, but they have minor short-term effects on
oases dynamics (Zhang \emph{et al.} , 2021; Zhou \emph{et al.} , 2017).
Two natural factors, precipitation and temperature, were collected from
National Tibetan Plateau Science Data Center (https://data.tpdc.ac.cn)
with 1 km spatial resolution throughout the growing seasons (from May to
July). Human factors had further impacts on oasis dynamics, but they
were limited in local ranges (Amuti \& Luo, 2014; Xiao \emph{et al.} ,
2019). Four human factors, total power of agricultural machinery, total
output value of the plantation industry, production of raw coal, and
population were acquired from the Xinjiang statistic yearbook, which
were collected during the same periods as LULC data.

\section*{3. Methodology}

{\label{methodology}}

\subsection*{3.1 Complex network}

{\label{complex-network}}

Complex networks are intricate structures composed of multiple
interconnected sub-systems with interaction occurring among them
(Newman, 2003; Xu \emph{et al.} , 2023). The complexity of these
networks is affected by the number of sub-systems involved and the
nature of their interactions (Wen \& Jiang, 2019). In this study, oasis
types are denoted as nodes, and their interactions are represented as
the edges. This model quantifies the relationship among nodes and
facilitates the analysis of the whole network system (Ramirez-Arellano
\emph{et al.} , 2021).

The flowchart of the oasis transition network construction in this study
was shown in Figure 2. The entire oasis transition process was viewed as
a complex network, and land types played the roles of sub-systems.
Transformation relations between land types were regarded as the network
edges. Depending on the edge direction, a complex network can be
categorized into directional and non-directional networks, as well as
weighted and non-weighted network based on the edge attributes.
According to the land use transition matrix in Tuha Basin, six
directional and weighted oasis transition network phases were created.
The oasis transition process, key land types and oasis structural
stability were then evaluated using three metrics: degree value,
betweenness centrality, and average path length.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure.2} Flowchart of oasis transition network construction

\subsubsection*{3.1.1 Oasis transition
analysis}

{\label{oasis-transition-analysis}}

In oasis transition network, the degree value of one oasis type node
refers to its edge number. The higher the degree value, the more
connections the node has with other nodes (Li \& Xiao, 2017). The
direction and intensity of the node interactions are important
indicators in a directional and weighted network. The weighted in-degree
and out-degree are defined based on the direction and weight from the
edges. Here, in-degree and out-degree represent the transfer-in and
transfer-out areas of oasis types, respectively. Weight refers to the
converted areas between two land types. The ratio of weighted out-degree
to weighted in-degree (named ratio) is employed to determine whether a
land type is a transfer-in or transfer-out type. If the ratio greater
than 1, the land type is defined as a transfer-out type; otherwise, it
is a transfer-in type. In-degree and out-degree can be calculated as:

where is adjacency matrix of the network. If there are any connections
between th node and th node, ; else, , here , \emph{N, n} represent the
number of nodes in a network, and when , . is the transition matrix. is
the element in , if represent the weighted out-degree from th node to th
node, else represent the weighted in-degree from th node to th node. The
number of , when equal to the out-degree of th node else equal to the
in-degree of th node.

\subsubsection*{3.1.2 Identification of key land
type}

{\label{identification-of-key-land-type}}

In the complex network, betweenness centrality is used to identify the
key node in oasis transition network, which refers to the ratio of the
shortest paths that pass through this node to all the shortest paths in
the network. The shortest path refers to the node sequence with shortest
weighted sum between two nodes, and its length is considered as the
smallest weighted sum between two nodes (Lambiotte \emph{et al.} , 2019;
Sporns, 2018). In the oasis transition network, a land type with higher
betweenness centrality would have more control over the whole network,
because of more information passing through that node. The key land type
plays a vital role in affecting the interactions of land types and the
oasis transition network vitality. Betweenness centrality can be
calculated as:

where is the th node betweenness centrality, is the number of shortest
path passing the th node, and is the number of shortest path in whole
network.

\subsubsection*{3.1.3 Oasis structural
stability}

{\label{oasis-structural-stability}}

Oasis type transition possibility demonstrates the oasis structural
stability in the complex network and the path length usually represents
the transition possibility of two types. Average path length are
employed to express the possibility of two types in this study
(Zhang\emph{et al.} , 2019). Here, the path length refers to the number
of edges in a sequence that connects two nodes in a network, and the
corresponding sequence with the shortest length between two nodes is
called the shortest path. The average length of the shortest path in a
network is the average path length. The shorter average path length, the
higher transfer efficiency is among the oasis types but a more unstable
oasis structure. The average path length was calculated as:

where is the average path length in the network. refers to the number of
nodes in the network. represents the length of the shortest path between
node and .

\subsection*{3.2 Driving forces for Oasis
transition}

{\label{driving-forces-for-oasis-transition}}

Oasis transition driving forces were explored from natural and human
aspects. Pearson's correlation coefficient (\emph{r} ) is used to
express the linear correlation between two variables (Cui \emph{et al.}
, 2023). In this study, we quantified the driving forces between driving
factors and oasis changes using \emph{r} -value. \emph{r} -value can be
expressed as:

where and represent oasis type and the area at the th year. is the
number of samples; and represent the mean value vectors of and ,
respectively. is the correlation coefficient between and .

\section*{4. Results}

{\label{results}}

\subsection*{4.1 Oasis changes in areas}

{\label{oasis-changes-in-areas}}

The area and proportion of oasis area in Tuha Basin for different oasis
types were counted from LULC data during1990 and 2020 in Tuha Basin. As
shown in Figure 3, the oasis in Tuha Basin expanded between 1990 and
2020, with the area increasing from 14346.43 km\textsuperscript{2} to
19977.09 km\textsuperscript{2}. Natural oasis expanded the most, with
the area increasing from 11219.26 km\textsuperscript{2} to 15922.68
km\textsuperscript{2}, the proportion increasing from 78.21\% to
79.71\%. Shrubland contributed the most expansion in natural oasis, with
the area and proportion increasing from 1121.93 km\textsuperscript{2} to
4756.68 km\textsuperscript{2} and from 2.01\% to 23.81\%, respectively.
Grassland was the largest oasis type, and averagely accounting for
3.91\% of Tuha Basin for seven periods.

Artificial oasis also had a slight rising trend, with the area
increasing from 3127.17 km\textsuperscript{2} to 4054.41
km\textsuperscript{2}, the proportion decreasing from 21.79\% to 20.29\%
during 1990 and 2020. Explicitly, cropland accounted the greatest number
of artificial oasis, the area changing from 2914.63
km\textsuperscript{2} to 3597.91 km\textsuperscript{2}, and the
proportion decreasing by 18.01\% in 2020 than that in 1990 with 20.31\%.
Artificial oasis expansion was mainly attributed to the increment of
built-up, with the area from 213.53 km\textsuperscript{2} increasing to
456.50 km\textsuperscript{2}, the proportion increasing from 1.48\% to
2.28\%.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image43/image43}

\end{center}
\end{figure}

\textbf{Figure. 3} Oasis area and proportion variations in Tuha Basin
from 1990 to 2020.

\subsection*{4.2 Oasis transition process
analysis}

{\label{oasis-transition-process-analysis}}

Oasis transition process was reflected in Figure 4 through nodes and
edges in the oasis transition network. The width of the edges in the
subgraphs referred to the converted areas from one node to another. The
complexity of the network was decided by the numbers of nodes and edges.
Generally, the interactions among all the land types became more
frequent from 1990 to 2020, with more edges connecting the nodes.
Grassland owned the most edges, proving the most active node in the
oasis transition network. The interaction between cropland and shrubland
was dramatic during the first three periods from 1990 to 2005, and kept
steady in the last three periods. There was no transfer-out of built-up
to other land types from 1990 to 2000, and the edge of the transfer-in
type was substantially wider than the transfer-out one throughout the
whole study period.

To reduce the redundancy of the network, principal component analysis
was conducted by calculating the main components nodes and edges that
had the edge weights with above-average values in the oasis transition
network (Figure 5). The primary conversions occurred among grassland,
cropland, shrubland, and Bare land. The interaction between grassland
and bare land dominated in the oasis transition network throughout the
whole study period; the transition between bare land and shrubland was
predominant after 1995. The weight of cropland gradually decreased until
it was below the average weight, at which point it was eliminated as a
primary component in the oasis transition network in the period of
2015-2020.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image44/image44}

\end{center}
\end{figure}

\textbf{Figure.4} Oasis transition networks in different periods\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image45/image45}

\end{center}
\end{figure}

\textbf{Figure.5} Oasis transition network principal components in
different periods

Table 1 showed the results of node ratios, out-degree and in-degree from
the oasis transition network to quantify the conversions among various
oasis types. A ratio less than 1 indicated a transfer-in oasis type in
the sub-period; if the ratio declined during 1990 and 2020, the
transfer-in area of the oasis type accelerated. A ratio greater than 1
showed a transfer-out oasis type; if the ratio grew during 1990 and
2020, the transfer-out area of the oasis type sped up.

Natural oasis and artificial oasis were expressed as transfer-in types
from 1990 to 2020, with ratios of 0.46 and 0.52, respectively.
Throughout the study periods, grassland had the highest out-degree and
in-degree values and the ratios were steadily round 1, implying the most
active land type in the oasis transition network. Shrubland had a low
ratio and increasing tendency in out-degree and in-degree throughout the
study period. The ratio of shrubland dramatically dropped from 1.10 to
0.19 from 2015 to 2020, indicating the shift from transfer-out to
transfer-in. The ratio of forest varied during the study period and it
peaked at 0.23 during 2015 to 2020. Generally, water body was a
transfer-out type, and the ratio fluctuated during 1990 and 2020. The
ratio of cropland was a transfer-in type with the ratio smaller than 1
after 1995. The out-degree and in-degree maintained in a high level and
changed slightly from 1990 to 2020. Built-up had the lowest ratio
(0.00-0.02), suggesting that the area of built-up was fixed, and it was
difficult to be converted into other land types.

\textbf{Tab.1} Node ratio, out-degree and in-degree in oasis transition
network\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Land type}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{1990-1995} \textbf{(Ratio, out-degree, in-degree)}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{1995-2000} \textbf{(Ratio, out-degree, in-degree)}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{2000-2005} \textbf{(Ratio, out-degree, in-degree)}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{2005-2010} \textbf{(Ratio, out-degree, in-degree)}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{2010-2015} \textbf{(Ratio, out-degree, in-degree)}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{2015-2020} \textbf{(Ratio, out-degree, in-degree)}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Cropland}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.75, 5, 4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.44, 6, 5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.92, 7, 5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.96, 6, 4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.62, 7, 6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.92, 7, 6\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Built-up}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.00, 0, 3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.00, 0, 4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.00, 2, 5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.01, 2, 4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.00, 2, 5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.02, 3, 6\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Forest}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.35, 2, 1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.57, 3, 2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.65, 3, 2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.74, 2, 2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.64, 3, 3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.23, 4, 5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Shrubland}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.32, 3, 3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.17, 4, 3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.32, 4, 3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.68, 4, 4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.10, 5, 3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.19, 6, 4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Grassland}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{0.91, 7, 6}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{0.80, 7, 6}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{1.09, 7, 7}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{0.95, 7, 7}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{0.95, 7, 7}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{1.15, 7, 7}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Water body}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.33, 2, 4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.73, 3, 2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.51, 4, 3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.39, 3, 3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.17, 3, 5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.34, 5, 5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Permanent ice and snow}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.50, 2, 1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.00, 1, 3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.92, 2, 4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.60, 4, 4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.48, 4, 3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.22, 6, 5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Bare land}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.97, 5, 4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.91, 6, 5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.19, 6, 6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.17, 6, 6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.16, 7, 6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.65, 7, 7\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

The oasis transition during 1990 and 2020 was exhibited in Figure 6,
which illustrated the distribution features of various oasis types in
Tuha Basin. Totally, the area of oasis converted to non-oasis was
3940.70 km\textsuperscript{2}; and 9570.38 km\textsuperscript{2} of
non-oasis converted to oasis, which demonstrated the oasis expansion in
Figure 3. Specifically, as an almost transfer-in type, the converted
area from non-oasis to artificial oasis was 1418.18
km\textsuperscript{2}, distributing in the southeast of Gaochang
district, surrounding of Yizhou district and northern Balikun county.
Natural oasis converted 3520.84 km\textsuperscript{2} to non-oasis,
mainly occurred in northern and eastern Balikun; as an important
transfer-in oasis, non-oasis converted 8152.20 km\textsuperscript{2} to
natural oasis, mainly concentrating in northern part of Balikun county.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image46/image46}

\end{center}
\end{figure}

\textbf{Figure.6} Transition of different oasis types in Tuha Basin
during 1990 and 2020 (a), and area of oasis transition statistic (b)

\subsection*{4.3 Identification of key land
type}

{\label{identification-of-key-land-type-1}}

Betweenness centrality was calculated for all nodes in the oasis
transition network (Figure 7) to reveal the importance of nodes and
identify the key land types in the oasis transition network. During the
six sub-periods, grassland had the highest betweenness centrality with
value 17.67, 11.33, 10.67, 12.17, 7.33, and 3.18, respectively, proving
the most important type in the oasis transition process. Grassland was
transferred in mostly from cropland and bare land, and transferred out
to shrubland. However, the betweenness centrality showed descend
tendency as the oasis expansion and more complicated topological
relationship among the land types. The second highest betweenness
centrality was cropland, with the values of 2.67, 3.83, 4.67, 3.33, 4.83
and 2.78, respectively during the six sub-periods. Betweenness
centrality of cropland was close to grassland in 2015-2020, becoming the
key land types in oasis transition network. Furthermore, the betweenness
centrality of bare land was 5.50 in 2015-2020, revealing that it was
also a key land type.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image47/image47}

\end{center}
\end{figure}

\textbf{Figure.7} Betweenness centrality of nodes in oasis transition
network during various periods

\subsection*{4.4 Oasis structural stability
analysis}

{\label{oasis-structural-stability-analysis}}

The oasis structural stability was assessed by the average path length
for all the sub-periods in the oasis transition network. If the average
path length was longer, the transition between the oasis types was more
difficult, and the oasis had a stronger structural stability. In Table
2, the average path length of the oasis transition networks was all
shorter than 1.5 in the seven sub-periods, implying an unstable
structure of the oasis system. Additionally, the average path length of
the oasis transition network descended from 1.47 to 1.20 in 1990-2020,
which demonstrated the oasis system became increasingly unstable.

The average path length decreased by 5.44\% between 1190-1995 and
1995-2000, and kept stable during 1995-2010. In this period, cropland
areas expanded significantly, and many efforts like planting. However,
the average path length decreased to 1.20 during 2010-2020, which
indicated transitions among different land types were easier and oasis
transition network was more complex.

\textbf{Tab.2 Average path length of oasis transition complex network}\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Periods & 1990-1995 & 1995-2000 & 2000-2005 & 2005-2010 & 2010-2015 &
2015-2020\tabularnewline
Average path length & 1.47 & 1.39 & 1.38 & 1.39 & 1.32 &
1.20\tabularnewline
\bottomrule
\end{longtable}

\subsection*{4.5 Driving factors for oasis
changes}

{\label{driving-factors-for-oasis-changes}}

The inter-annual variations of driving factors during 1990 and 2020 were
displayed in Figure 8. the average monthly temperature in growing season
ranged from 15.64 0\selectlanguage{english}C and 22 0\selectlanguage{english}C to 20.15 0\selectlanguage{english}C and 25.79 0\selectlanguage{english}C. The mean
temperature in growing season ranged from 19.22 0\selectlanguage{english}C to 23.07 0\selectlanguage{english}C, with a
slight upward tendency. Precipitation decreased obviously from 1990 to
2020, and the variations in growing season ranged from 1.44 mm and 16.99
mm to that of between 2.77 mm and 6.24 mm. In growing season,
precipitation expressed as significant decreased tendency. The four
human driving factors- population, total power of agricultural
machinery, production of raw coal and total output value of plantation
industry, all showed upward tendency over the study period. The
production of raw coal and the total output value of the plantation
industry were increased dramatically after 2005.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image48/image48}

\end{center}
\end{figure}

\textbf{Figure.8} Annual variations of different driving factors

Pearson's coefficient and \emph{t} -test were utilized to express the
relevance between oasis transitions and driving factors in Figure 9.
Among the natural driving factors, oasis transitions showed positive
correlation with temperature (\emph{P} -value \textless{} 0.05,
\emph{r}= 0.88) and insignificant correlation with precipitation.
Temperature was positively related to forest (\emph{P} -value
\textless{} 0.01,\emph{r} = 0.94) and shrubland changes (\emph{P} -value
\textless{} 0.01,\emph{r} = 0.95). Notably, precipitation had negative
effects (\emph{P} -value \textless{} 0.05) on shrubland changes. It
indicated that the most natural oasis changes in Tuha Basin was highly
sensitive to temperature, and scarcely affected by precipitation due to
the extremely low precipitation and its downward trend during 1990 and
2020 over Tuha Basin. Generally, natural factors mainly affected natural
oasis with the average coefficient of 0.92 (\emph{P} -value \textless{}
0.01), and had insignificant effects on artificial oasis.

All the human driving factors, population, total power of agricultural
machinery, raw coal production, and total agricultural output showed
positive correlations with the oasis changes, with the coefficients of
0.83, 0.94, 0.90 and 0.91 (\emph{P} -value \textless{} 0.05),
respectively. Cropland, shrubland and built-up changes were mainly
controlled by human activities, and built-up had the most positive
response to population, total agricultural machinery power, raw coal
production, and agricultural output, with the coefficients of 0.93,
0.94, 0.96 and 0.98 (\emph{P} -value \textless{} 0.01), respectively.
Shrubland changes were influenced significantly by the total power of
agricultural machinery, raw coal production, and agricultural output
value, with the coefficients of 0.87, 0.89, and 0.87, respectively
(\emph{P} -value \textless{} 0.05). With the same coefficient of 0.89,
human activities had obvious driving forces on both natural oasis and
artificial oasis. Human activities mainly affected shrubland in natural
oasis; the four human factors showed significant positive correlations
on artificial oasis, with the coefficients of 0.86, 0.86, 0.85 and 0.89,
respectively (\emph{P} -value \textless{} 0.05). The spatial allocation
of water resources improved with the development of agriculture and
industry of Tuha Basin. Moreover, the policies supporting the shrub
planting and growth were encouraged to combat desertification and
reinforce sand.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image49/image49}

\end{center}
\end{figure}

\textbf{Figure.9} Coefficients matrix between oasis changes and driving
factors during 1990-2020 (Pre: Precipitation, Tmp: Temperature, Pop:
Population, TPAM: Total power of agricultural machinery, PRC: Production
of raw coal, TPI: Total output value of plantation industry; *
represents \emph{P} -value \textless{} 0.05, and ** represents\emph{P}
-value \textless{} 0.01)

\section*{5. Discussion}

{\label{discussion}}

In this study, we constructed a directed and weighted complex network,
naming the oasis transition network, which was used to express the
transition patterns between different oasis types, identify the key land
type and quantify the oasis structural stability. Compared with
traditional transition analysis, the complex network described the
dynamic process and the influences of different oasis types in the oasis
system. Between centrality was used to identify the key land type, which
was explained the possibility the land type being the transitional type.
The findings of the oasis transition network indicated that the oasis
transitions were increasingly complicated from 1990 to 2020. Grassland
was the key oasis type in the oasis transition process, and the
importance decreased during 1990 and 2020. Bare land had the highest
betweenness centrality in 2015-2020 and cropland ranked third among all
the land types, indicating the important roles and the changes of key
land type in the transition network. According to the relevant report
and statistic year book published by the beau of statistic in Turpan and
Hami region, through the thirteenth `five-year' plan, greening
construction projects like returning farmland to grassland,
afforestation, combating and controlling desertification and three north
protective forests have gained significant effectiveness (Lyu \emph{et
al.} , 2020). During that period, grassland was not the most important
transitional land type due to the efforts of planting and reclamation of
the bare land.

Notably, in the oasis transition network, the land types with low
proportion of coverage also contacted with other land types frequently.
For example, shrubland expressed high degree values and was one of the
principal components in the oasis transition network and the
interactions with bare land dominated during the sub-periods of
1995-2000, 2000-2005, 2005-2010, 2010-2015 and 2015-2020. According to
the local statistic yearbook, a large proportion of shrubland were
planted artificially to combat wind and sand at the edge of the oasis
and against land degradation in the inner oasis, like shuttle and camel
thorn; the area of shrubland showed a distinct increasing trend in
2015-2020. We were inspired those human forces dominated in the oasis
transition process for most sub-periods.

Oasis structural stability was quantified using average path length in
the oasis transition network, which was a crucial index to express the
oasis sustainability in dryland. So far, few studies had investigated
the structural stability of the entire oasis system in dryland. In this
study, we applied the average path length to reveal the difficulty of
the interactions among the oasis types. With the results of more
unstable oasis structural stability, we concluded that there were easier
transitions among different land types recent years than before.
Therefore, we suggest that more attentions on key land types (e.g.
grassland) should be paid for the dryland sustainable development.

Even though the correlations between oasis and precipitation were
insignificant, water resource was one of the most important restraint
factors in dryland development (Yang \emph{et al.} , 2021). The surface
water in Tuha Basin is obviously shrinking recent years partly because
of the increasing temperature and intense evaporation (Rodell \emph{et
al.} , 2018; Wang \emph{et al.} , 2023) in Tuha Basin. Additionally,
water resources were over-exploited and underground water level had
continuously dropped for agricultural irrigation and mining (Hu \emph{et
al.} , 2019; Wang \emph{et al.} , 2022; Cheng \emph{et al.} , 2023). A
more adaptive strategy for regulating cropland areas and water resource
allocation is needed. From the driving factors analysis, population
played a key role in oasis expansion, for example, to meet the
requirements of the fast-growing population, the urbanization including
artificial reclamation and construction speeded up, making built-up a
most transfer-in type in the oasis transition network. Human factors
including policy factors, economic factor and cultural factors were all
important in the maintaining of the entire oasis system and land
optimization in drylands. Therefore, further survey and analysis for the
oasis driving should be deep considered.

\section*{6. Conclusion}

{\label{conclusion}}

In this study, oasis spatial-temporal variations in Tuha Basin were
first analyzed for the further transition investigation over the past 30
years. All the oasis types increased from 1990 to 2020, and the natural
oasis expanded the most. Oasis transition processes, key land type and
oasis structural stability were then depicted using the constructed
oasis transition network. We concluded that the oasis transition
relationship among different oasis types became more complicated in 2020
than that was in 1990. Grassland, shrubland, forest and cropland changed
the most during the study period, and grassland was viewed as the key
land type due to the high out-degree, in-degree values and betweenness
centrality. The oasis structural stability calculated from the network
showing declining trend from 1990 to 2020. To explore the driving forces
of oasis changes, correlation analysis between the natural and human
factors and oasis changes was conducted using the Pearson coefficient.
Among the natural driving factors, temperature correlated positively
with natural oasis changes, and had insignificant effects on artificial
oasis. In human driving factors, population, the total power of
agricultural machinery, raw coal production, and the total agricultural
output improved the growth of shrubland and the artificial oasis;
built-up showed the most positive response to human driving factors. The
study clarified the oasis transition pattern and process, and provided
scientific reference for future oasis study and management.

\section*{Acknowledgments}

{\label{acknowledgments}}

This study was supported by the Basic Resource Investigate Project of
the Ministry of Science and Technology: Land resource carrying capacity
and ecological agriculture investigation and assessment of Turpan-Hami
Basin (2022xjkk1100). Thanks for the data support from the LUCC data
(ESSD - GLC\_FCS30: global land-cover product with fine classification
system at 30 m using time-series Landsat imagery (copernicus.org)),
precipitation and temperature data from Loess plateau science data
center, National Earth System Science Data Sharing Infrastructure,
National Science \& Technology Infrastructure of China.
(http://loess.geodata.cn).

\section*{Conflict of Interest}

{\label{conflict-of-interest}}

The authors declare no conflict of Interest.

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