Page 148 - Çevre Şehir ve İklim Dergisi İngilizce - Özel Sayı

Determination of Erosion Statistics by Land Use Type at National Scale With the
Dynamic Erosion Model and Monitoring System (Demms)

Emergence of soil erosion depends on the effect of various factors. The
activities to control erosion require knowing both these factors and the
methods to control these factors (Renard et al., 2011). Studies and plans about
soil use and management and models, which aim to predict the amount of
erosion in general, are important to slow down the erosion processes (Tağıl,
2009). The equations, developed to calculate soil erosion, can be categorized
as the models based on conceptual and physical processes (Kinnell, 2010).
Today, RUSLE is the model which is acknowledged for the determination of
soil erosion amount and its assessment according to severity classes and is
also commonly used in many studies (Renard et al., 1994).
In this study also, the RUSLE technology was used because of its integration
with the Geographic Information Systems (GIS) (Yitayew et al., 1999), compatibility
with different scales (Lim et al., 2005), and being one of the most commonly used
models (Tiwari et al., 2000; Tağıl, 2007). The RUSLE model is commonly used in
the determination of soil erosion and its severity classes in Türkiye (Tüfekçioğlu
and Yavuz, 2016; Kızılelma and Karabulut, 2014; Tağıl, 2009; Irem et al., 2007).
The studies to determine the erosion risk with the USLE/RUSLE technology
have been done in Türkiye at the scope of small basins for a long term (Erdoğan
et al., 2007; Tunc and Schröder 2010a, b; Hacisalihoglu 2010; Özcan et al., 2015;
Saygin et al., 2014; Efe et al., 2008; Yüksel et al. 2008; Karaburun et al., 2009;
Demirci and Karaburun, 2012). Saygin et al. (2014) used sediment delivery ratio
(SDR) along with the RUSLE model to evaluate both potential and real soil
erosion risks and predicted the amount of sediment which was carried to the
dam reservoir from the slope of the basin. In the study completed in semi-arid
Çankırı-İndağı basin, data analyses were made with a nomograph according to
RUSLE-K’s land use adjustments and soil characteristics (Wischmeier and Sith
1978; Renard et al., 1997), and it was found that K values display differences
statistically (Bayramin et al., 2008). Erdoğan et al. (2007), in a study which they
conducted in order to predict the amount of erosion in a semi-arid agricultural
basin at the scale of 1:25.000, used Türkiye’s soil database (GDPS, 1986) in
order to predict the (R)USLE-K values, consisting of the combination of texture
and erosion classes. In a comprehensive study conducted by them, Erpul et
al. (2016) determined and reported the erosive impact of rainfall based on the
country-wide state meteorology stations’ long-term data and high-temporal
resolution records, and afterwards, the resulting data were successfully
integrated to the system for global rainfall erodibility assessment (Panagos et
al., 2017). While the RUSLE-C factor is directly related to the amount of erosion,
it can vary based on the type of land use (Kavian et al., 2015). In a study in
Afghanistan, which has a mountainous and rough topography, three different
approaches to the RUSLE-LS factor were tested, and it was found that erosion
has a positive correlation with slope (Ensari and Tayfur, 2023).

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