Impacts assessment of land cover
and land use changes on soil erosion changes (2005 - 2015) in Pakistan
Hammad Gilani 1, *, Adeel Ahmad 2,
Isma Younes 2 and Sawaid Abbas 3 *
1 Institute of Space Technology, Islamabad 44000,
Pakistan
2 Department of Geography, University of the Punjab,
Lahore 54590
3 Department of Land Surveying and Geo-Informatics,
The Hong Kong Polytechnic University, Kowloon, Hong Kong
* Correspondence:
hammad.gilani@mail.ist.edu.pk
&
hammad.gilani@gmail.com
Author Contributions: Hammad Gilani: Conceptualization,
Methodology, Supervision, Writing- Reviewing and Editing. Adeal
Ahmad: Software, Formal analysis, Validation. Isma Younes:writing—review and editing, Sawaid Abbas: writing—review,
and editing. All authors have read and approved the final version of
this paper. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding.
Acknowledgments: We would like to express our thanks to the
management of the Institute of Space Technology (IST), University of
Punjab, and the Hong Kong Polytechnic University for their support.
Conflicts of Interest: The authors declare no conflict of
interest. The views and interpretations in this publication are those of
the authors, and they are not necessarily attributable to their
organizations.
Abstract
Abrupt changes in climatic factors, exploitation of natural resources,
and land degradation contribute to soil erosion and Land Cover and Land
Use Changes (LCLUC). This study provides comprehensive analysis of
annual soil erosion dynamics in Pakistan for 2005 and 2015 using freely
available climatic, topographic, soil type, and land cover geospatial
datasets at 1 km spatial resolution. A well-accepted and widely applied
Revised Universal Soil Loss Equation (RUSLE) was implemented for the
annual soil erosion estimations and mapping by incorporating six
factors; rainfall erosivity (R), soil erodibility (K), slope-length (L),
slope-steepness (S), cover management (C) and conservation practice (P).
We used a cross tabular or change
matrix method to assess the annual soil erosion (ton/ha/year) changes
(2005 - 2015) in terms of areas and spatial distributions in four soil
erosion classes; i.e. Low (<1), Medium (1–5], High
(5-20], and Very high (>20). For conservation and
effective ecosystem services, at seven administrative units of Pakistan
temporal and spatial bivariate analysis were carried out among soil
erosion change and LCLUC. Major findings of this paper indicated that,
at the national scale, an estimated annual soil erosion of 1.79 ± 11.52
ton/ha/year (mean ± standard deviation) was observed in 2005, which
increased to 2.47 ±18.14 ton/ha/year in 2015 with total 29,081
km2 (3.30%) loss and 17,506 km2(2.00%) gain in LCLUC classes between 2005 - 2015. Spatially explicit
and temporal annual analysis of soil erosion and LCLUC could be used for
soil conservation and management practices, environmental impact
assessment studies, among others.
Keywords: Soil erosion; Geospatial datasets; RUSLE; LCLUC; Soil
conservation; Pakistan.
1. Introduction
Injudicious
exploitation of natural resources leads to land degradation, droughts,
floods, deforestation, etc. The shrinking per capita natural resources
lead to intensive land use and resultant in further environmental and
land degradation. Land degradation is increasing in severity and extent
in many parts of the world, with more than 20% of cultivated areas,
30% of forests, and 10% of grasslands undergoing degradation (Baiet al. , 2008). The decline in land quality caused by human
activities has been a major global issue since the
20th century and will remain high on the international
agenda in the 21st century (Eswaran et al. ,
2001). Increasing cropping intensity, nutrient mining, traditional
agricultural practices, and other human interventions are causing
different kinds of land degradation that threaten livelihoods, food
security, people’s health, and long-term Sustainable Development Goals
(SDGs) of promising countries. Land degradation by landslides, soil
erosion, and internal biophysical and chemical deterioration are the
main constraints for sustainable Land Cover and Land Use (LCLU)
management and practices (Kapalanga, 2008).
Soil erosion, considered as an important land degradation process, is
the loss of top fertile surface soil as a result of erosive rainfall and
consequent runoff, deforestation, sea-land intrusion (Ganasri & Ramesh,
2016; Saha et al. , 2018). Soil erosion is a combination of
detachment and transport of soil particles and is defined as the amount
of soil lost in a specified period over an area of land which has
experienced net soil loss (Galdino et al. , 2016; Nam et
al. , 2003; Pimentel, 1993; Saha et al. , 2018). Overall, it has
negative impacts on the environment through soil nutrient losses, water
quality deterioration, and agricultural production, which ultimately
leads to economic costs and loss of lives. Assessment and mapping of
soil erosion are considered useful information to develop spatial
priority areas for controlling and implementing soil erosion mitigation
practices.
From simple to complex empirical models have been developed and used to
quantify the soil erosion, which includes Zengg equation (Zingg, 1940),
Universal Soil Loss Equation (USLE) (Wischmeier & Smith, 1978),
Chemical Runoff and Erosion from Agricultural Management Systems
(CREAMS) (Knisel, 1982)), Modified Morgan, Morgan and Finney (MMMF)
(Morgan et al. , 1984), Agricultural Nonpoint Source model (AGNPS)
(Young et al. , 1989), Revised Universal Soil Loss Equation
(RUSLE) (Renard et al. , 1991), Unit Stream Power-based Erosion
Deposition (USPED) (Mitasova et al. , 1996), European Soil Erosion
Model (EUROSEM) (Morgan et al. , 1998), etc. The selection and
implementation of a particular soil erosion model/equation depends upon
the data availability, spatial and temporal scale of the application
which may have certain limitations. Among all the soil erosion
models/equations, RUSLE is being most widely used due to its simplicity,
data requirements, and precision (Ghosal & Das Bhattacharya, 2020;
Renard et al. , 1991). It was developed and designed with the
basic structure of the USLE equation with several improvements in
determining factors i.e. rainfall erosivity (R factor), soil erodibility
(K factor), slope-length (L factor), slope-steepness (S factor), cover
management (C factor) and conservation practice (P factor). Based on
geospatial datasets and environments for rapid as well as a detailed
assessment of soil erosion at diverse spatial scales these factors can
be acquired, processed, and utilized in the RUSLE (El Jazouli et
al. , 2017; ESA, 2018; Gelagay & Minale, 2016; Uddin et al. ,
2016, 2018; Ullah et al. , 2018).
In South Asia, Pakistan has been continuously suffering from natural
disasters (floods, earthquakes, droughts, etc.), morbid socioeconomic
activities (increased population, unsustainable land management
practices, overgrazing, deforestation, etc.), biophysical factors
(unfavorable geology, topographical variations, etc.), and emerging
effects of climate change observed through uncharacteristic patterns of
weather conditions (Anjum et al. , 2010). According to the Global
Climate Risk Index (GCRI), Pakistan is the 5th most
vulnerable country to climate change (Eckstein et al. , 2019).
Forest cover removal accelerates surface erosion besides its negative
impact on biodiversity and human ecology an increase. Even in the flat
to gentle areas of Pakistan, unplanned conversion of agriculture and
rangeland to built-up is leading to the severity of soil erosion
(Abuzar, 2012; Ahmad et al. , 2012). On USLE or RUSLE, Alewell et
al., (2019) synthesized and reported peer-reviewed published studies in
40 years (1977 to July 2017), Pakistan contributed only one or two
peer-reviewed studies (see figure 1 in Alewell et al., (2019)).
Within Pakistan, total four
studies over the five sites have been conducted on soil erosion
estimation and mapping using the RUSLE at various spatial scales, using
multi-resolution geospatial datasets (Abuzar et al. , 2018;
Ashraf, 2020; Ashraf et al. , 2017; Nasir et al. , 2006;
Ullah et al. , 2018). Most of these studies were carried out with
one-time assessments with limited spatial coverage (watershed,
sub-basin, catchment, etc.) (S1 and Figure S1). Using the RUSLE,
Borrelli et al. (2017) produced global scale soil erosion maps at 25 km
spatial resolution for the years 2001 and 2012. Global soil erosion maps
are spatially too coarse for the national level to sub-national scales
decision making and management practices.
In Pakistan, at the national scale, soil erosion dynamic estimations and
quantifications are a constraint due to several reasons including lack
of data availability and processing willingness, topographical
roughness, wilderness, and diversity, etc. The study is designed to
cater, at the national scale and seven administrative units (Azad Jammu
& Kashmir, Balochistan, Gilgit-Baltistan, Islamabad Capital Territory,
Khyber Pakhtunkhwa, Punjab and Sindh) in Pakistan, annual soil erosion
dynamics from 2005 to 2015 at 1 km spatial resolution using freely
available topographic, biophysical, and climatic variables in the
RUSLE. At the national and
administrative units, impact assessment of Land Cover and Land Use
Changes (LCLUC) on soil erosion was quantified and reported that could
be utilized for better management, conservation, and restoration of
natural resources through introducing the cost-effective, long term, and
equitable ground interventions and practices.
2. Materials and methods