INSERT TABLE 5 HERE
Out of some relevant approaches, on-site field observation and
biophysical measurement are labour intensive, costly and time-consuming.
Therefore, these tactics are likely applicable over small spatial areas
(e.g. field scale). However, they are able to provide first-hand
measurements and observation records on the dynamics and conditions on
desertification, which can be used for validation of modelling results
and remote sensing interpretation.
On-site field observations are also capable of detecting changes (e.g.
soil properties, soil-water condition, etc.) induced by desertification
that cannot easily be investigated via other methods (Zhang and
Huisingh, 2018). Long-term on-site field observations are useful for
investigations of desertification mechanisms and the response of
desertification to different treatment/mitigation measures (e.g. Li et
al., 2000, Su et al., 2010). It is important for desertification
evaluation and control to establish permanent field experimental
stations. In China, the “Chinese Desert Ecosystem Research
Network ” that comprised with 43 National Research monitoring stations
located across the N and NW China (Guo et al., 2014).
Desertification is often assessed via indirect indicators such as its
driving factors (e.g. drought, anthropogenic activities), and impacts
(e.g. vegetation cover change etc.) (Ajai et al., 2009, Šarapatka et
al., 2010, Kust et al., 2011, Jiang et al., 2019a, Jiang et al., 2019b,
Gul and Ersahin, 2019). Monitoring of drought/aridification is a common
way to evaluate the risk of desertification in regions that have been
suffering from drought like countries in Western Asia (Erian et al.,
2011, Elagib and Abdu, 1997), in S Asia (Shahid and Behrawan, 2008), E
Asia (Wang et al., 2017) and E Europe (Lupu et al., 2010, Mateescu et
al., 2013). Many indicators of drought have been developed, such as
“Aridity Index ” (Park et al., 2018), “Standardized
Precipitation Index ” (SPI); “Standardized
Precipitation-Evapotranspiration Index ” (SPEI) and “Palmer
Drought Severity Index ” (PDSI) (Wang et al., 2017). Among these
indicators, the SPI has been implemented in the Baribo Basin, Cambodia
and showed that the extremely drought events occurred in 1987, 1993,
2001 and 2004 respectively (Sok et al., 2017).
Besides, the indicators of SPI, SPEI and PDSI were applied to
investigate the development of drought in the dryness region of NW China
between 1960 and 2010 (Wang et al., 2017). Furthermore, the composite
index has been applied to assess the desertification risk in the BRI
region, which considers multipole natural and anthropogenic factors and
thus provides a more comprehensive evaluation of desertification (Hien
et al., 2019). For example, the “Environmentally Sensitive Area Index”
(ESAI), which is estimated as the geometric mean of four quality indexes
on soil, climate, vegetation and management, has been used by (Jiang et
al., 2019a) to assess the desertification risk of the C Asia.
“Remote sensing ” is an effective tool for spotting and
distinguishing indicators of various stages of desertification processes
through capturing the changes caused by desertification (e.g. changes in
vegetation coverage) (Zhang and Huisingh, 2018, Albalawi and Kumar,
2013, Wei et al., 2020). Remote sensing data, often freely accessible,
have been applied in the BRI countries for the desertification
evaluation (e.g. Li et al., 2013, Liu et al., 2004).
Indices
like the “Normalized Difference Vegetation Index (NDVI) ”
(NDVI), “Enhanced Vegetation Index (EVI)”, “Fractional
Vegetation Coverage ” (FVC), “Lead Area Index ” (LAI) derived
from remote sensing datasets are commonly used indicators (Shalaby et
al., 2004, Zhang and Huisingh, 2018). For example, the
“Desertification Risk Index ” (DRI), derived from climate
factors and NDVI, was developed by Feoli et al. (2000) for
desertification evaluation in Turkey and has been successfully applied
in the Middle East Lebanon (Dragan et al., 2005).
Remote Sensing technologies have been widely adopted to investigate and
understand the changes of soil properties, for example, soil organic
matter content (Kumar et al., 2017, Mirzaee et al., 2016) and soil
texture (da Silva Chagas et al., 2016, Wu et al., 2015), further
improving the capability of remote sensing in desertification detection.
However, Remote Sensing technology is subject to inaccuracy. For
example, the NDVI and other vegetation information-based methods often
overestimate desertification in sparsely vegetated areas mainly owing to
the unsteadiness of seasonal vegetation changes and the intensity of
precipitation, and the rainstorm-caused severe effects (Dawelbait and
Morari, 2008, Wessels et al., 2012, Wei et al., 2020).
Some models have been employed for evaluating the desertification in the
BRI countries that are the soil erosion / desertification models such as
the “Revised Universal Soil Loss Equation ” (RUSLE),
“Mediterranean Desertification and Land Use” (MEDALUS) . TheRUSLE, developed by Renard (1997), estimates soil erosion rates
driven by “Rill and Interill Erosion ” based on factors
including, “Rainfall Erosivity ”, “Soil Erodibility ”,
“Topography ”, “Vegetation Cover ” and “Erosion
Control ”. That model has been popularly applied worldwide to assess
soil erosion rates through the cascade of hillslopes study area (Borelli
et al., 2017, Li et al., 2020). The model has also been adopted in the
BRI countries, Myanmar (Tun et al., 2015), Thailand (Wijitkosum et al.,
2013) and China (e.g. Sun et al., 2014, Fu et al., 2011, Li et al.,
2020). Whilst, the MEDALUS model was developed during the
“MEDALUS EU-funded project” (Kirkby et al., 1998) and can be
effectively used to identify areas of desertification, in relating to
various parameters (including “landforms, soil, geology,
vegetation, climate and human actions ”). The MEDALUS model was
developed for the Mediterranean climatic conditions, such as in Greece
(Karamesouti et al., 2018). It has now been widely applied in the BRI
countries outside the Mediterranean region such as Iran (Zehtabian et
al., 2005, Sepehr et al., 2007) and Thailand (Wijitkosum, 2016).
Another type of model approach is via climate circulation models, which
were applied in the BRI area. For example, the “National Centre
for Ocean-Land-Atmosphere Studies ” developed the Atmospheric General
Circulation Model (Xue, 1996), namely the “Community Atmospheric
Model Version 3 ” (CAM 3) to assess climatic change and its possible
impacts on desertification (Jin et al., 2012). The “NationalCentre for Ocean-Land-Atmosphere Studies ” also further adopted
some other atmospheric general circulation models, including a biosphere
model by Xue (1996), which tested the response of regional climate to
the desertification in the Mongolian Plateau. Other modelling results
found that the surface water and energy balance influenced by
desertification. The CAM3 modelling results also demonstrated that
dryness level in C Asia was closely related to declined wind velocity
and decreased evaporation upstream from the Mediterranean Sea, Black
Sea, and Caspian Sea in Boreal Winter (Jin et al., 2012). Whilst, the
“Intergovernmental Panel on Climate Change ” (IPCC) models
(Doutreloup et al., 2011, Miao et al., 2015) also reported that mean
temperature in the Province of Binh Thuan, SE Vietnam was projected to
rise by about 1.6 °C (over 2046-2065) and 2.5 °C (over 2081-2100) and
the area is likely to suffer from extreme temperatures and extreme
rainfall events (Doutreloup et al., 2011).
Most of contemporary models either simulate only one form of
desertification (i.e. soil erosion) or evaluate desertification through
prediction of climate change. Few quantitative models have been
developed to directly assess the desertification status. To the authors’
knowledge, the MEDULAS is the only model that can be used to
quantitatively assess the desertification condition. The model is
applied under the empirical research perspectives, which constraints its
capability of reproducing different desertification processes and the
response of these processes to natural and socio-economic factors such
as climate condition (precipitation, temperature), vegetation growth,
land use, human population etc. (Khachatryan, 2013, Li et al., 2017, de
Vente et al., 2013). Unfortunately, we have to understand that these
tools and models are still having factual limitations. For example, they
have an intensive input data that limits their application because of
lacking sufficient data support (Li et al., 2017). Field investigations
are appropriate for an exhaustive understanding on the processes and
mechanisms of desertification over a small area that is beneficial for
the development of desertification models. Indirect detection, unlike
field investigations, assesses desertification situation based on
relevant indicators, which, however, are sometimes not available. Remote
sensing is suitable for detection of desertification indicators over a
large area, given that remote sensing images have large spatial
coverage. Their applicability is often restricted by the technical
issues, such as the accuracy and resolution of the images (Albalawi and
Kumar, 2013). Quantitative and mathematical models can be adopted to
assess and/or predict desertification situation at various spatial
scales. The comprehensive desertification model is still rare. Some
models that incorporate the reciprocal relationships among natural and
socio-economic factors and their influences on desertification are still
desirable. Besides, the models often require massive input data, which
limit their application in places without sufficient data support.
Overall, the desertification evaluation methods are of their own
characteristics and applicability (Table 5), and the selection of
methods should be based on an evaluation of the nature of research and
data availability.
6. Conclusion
In this article, we studied the desertification and its control in the
countries associated with the BRI initiative proposed by China in 2013,
via an extensive review of literature. We argued
that the desertification has been
widely spread in the BRI countries, particularly in the Central Asian
countries, despite quantitative assessment and prediction of
desertification is still limited. We also found that the most important
factors of desertification by natural and anthropogenic processes are
dry climate condition and mis-land-use/management respectively.
Anthropogenic (human-induced) activities are a crucial driving force for
land desertification in the BRI countries, while natural factors
dominated in some dryland regions. However, the relative importance of
natural and anthropogenic factors has yet been (quantitatively) assessed
for most of the BRI countries.
Desertification in the BRI region has a series of negative impacts on
socio-economy and eco-environment, mainly including loss of GDP, loss of
productive land and regional climate. Most of the BRI countries have
recognized the desertification issue and have ratified the UNCCD and
worked out the NAPs. The comprehensive desertification
mitigation/conservation programmes are still lacking. BRI countries are
therefore encouraged to work together under trans-national or
cross-boundary cooperation framework to combat desertification. Research
methods employed for desertification evaluation include quantitative
approaches (e.g. via mathematical models), indirect detection, and
direct observation and biophysical measurement (e.g. measuring the tree
and vegetation growth). They have their own characteristics and
applicability, and the selection of methods should be based on an
evaluation of the nature of research and data availability.
Overall, the desertification situation and their driving mechanisms
should be thoroughly evaluated for the BRI region. The all-inclusive and
rational conservation measures should be recognised and implemented, in
prior to lessen the influence of climatic change and possible human
interventions under the BRI framework.