Evaluating land degradation by gully erosion through soil erosion indices and rainfall thresholds

doi:10.21203/rs.3.rs-2216407/v1

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Evaluating land degradation by gully erosion through soil erosion indices and rainfall thresholds

https://doi.org/10.21203/rs.3.rs-2216407/v1

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published 08 May, 2023

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Among the land degradation processes, gully erosion is the one that poses more environmental and societal challenges in arid regions. Predicting spatio-temporal gully development in a region under changing conditions is important to adopt proper mitigation measures. Here we investigate the Ghapan-Olya watershed in Golestan province in Iran, which is impacted by many erosional landforms, including gully phenomena. We applied the pixel-based distributed LANDPLANER model, to predict the location of rainfall induced gullies. Different input maps were prepared using UAV data, the region's soil, and seasonal land use information. Our modelling results from the application of topographic thresholds and the erosion index calculation were compared with field observations in locations with and without gullies and the application of quantitative metrics such as sensitivity, specificity, fallout, precision, and recall. Our study revealed that the spatial density of the gully’s location is more repetitious in the areas with altitude about 200-300m, steeper slope (between 15 to 30 degrees), and lower average accumulation value (< 100) in the south-east facing slope. Higher values of erosion index and topographic threshold were obtained for the min curve number, where intense rainfall is more frequent and where land use and cover conditions are more favourable for gully occurrence. The greater values of soil erosion indices were provided for the CN3 scenario (autumn) when the daily rainfall is 80 mm (6.27), continued by the summer scenario (CN2) with 80 mm daily rainfall (4.88), and spring (CN1) again with 80 mm daily rainfall (2.99). In addition, topographic threshold maps illustrate the greatest amount of soil erosion for the CN scenario (without considering daily rainfall) in autumn. Our results provide accurate prediction parameters to simulate the natural hazard under changing conditions.

Gully erosion

LANDPLANER modelling

Geomorphic analysis

Land use/land cover

Erosion index

Topographic threshold

Soil erosion is a geomorphologic process that leads to landscape development and is considered by soil specialists and soil conservation researchers one of the main land degradation processes (Saha et al., 2022), and a relevant issue faced by modern society in many parts of the world. So far, there have been many studies on erosion (e.g., causes of its formation and extension, measurement of its dimensional changes) using statistical (regression) relationships, modelling (such as neural network, empirical, machine learning, basic knowledge), or based on methods of collecting (field and laboratory) information and using remote sensing techniques (comparing satellite images of the target range) (Chowdhuri et al., 2020; Roy et al., 2022). Experimental relationships have also been suggested to forecast different erosion rates (Pal et al., 2020; Roy et al., 2020; Pourghasemi et al., 2020). A large discrepancy has been reported in the results of erosion calculations due to the lack of attention to the role of soil, rock or formation, land use change and management, which makes it difficult to recognize different variability of the types of erosion (Garosi et al., 2018; Sidle et al., 2019; Dube et al., 2020; Vanmaercke et al., 2021).

Gully erosion has a significant role in causing land degradation threatening agricultural sustainability (Garosi et al., 2018). Gully erosion and associated processes wear away most of the nutrient-rich topsoil into streams causing a loss of agricultural productivity and accumulation of sediments in the reservoir (Garosi et al., 2018). In arid regions, including Iran, gully erosion causes extensive desertification and disrupts the whole ecosystem services (Hosseinalizadeh, et al, 2020; Karydas and Panagos, 2020). Gully erosion can also affect society and economy (Faulkner, 2006; Wilson, 2011). Thus, mapping areas potentially interested by gully erosion is relevant for soil conservation and water resources management (Saha et al., 2020). In addition, the forecast of spatial and temporal occurrence of gully erosion has attracted significant attention (Kariminejad et al., 2019). Gully erosion is an intricate geomorphological process, often related to some environmental variables including soil texture, land cover and land use, climate and anthropogenic factors (Arabameri et al., 2020; Chowdhuri, et al., 2020; Chakrabortty et al., 2022). Among all the triggering factors, precipitation is regarded to be the primary for all erosional processes (De Roo et al., 1996; Peruccacci et al., 2017). Only limited detailed studies have examined the diverse factors through a common modelling framework (Chowdhuri, et al., 2020; Roy et al., 2020; Kariminejad et al., 2020; Saha et al., 2021; Chakrabortty et al., 2022). In this research, we use the LANDPLANER (LANdscape, PLants, LANdslide and Erosion) (Rossi, 2014) to predict and analyze the gully erosion occurrence in a study area in Iran. The choice of LANDPLANER model among others is due to the possibility of using multiple raster-based modelling approaches, its reduced input demand, and the easiness to perform scenario-based analyses (Rossi, 2014; Agostini et al., 2022; Kariminejad et al., 2020). The model is devised to use rainfall information, hillslope geomorphometric characteristics derived from a DEM and a curve number map to account for vegetation, soil and land use information. In this study, the model was applied using very high-resolution DEM data obtained from UAV, potentially able to intercept processes on a very detailed scale. The advantage of using UAVs include their capability of imaging similar to manned aircraft and satellites, but with a reasonable application flexibility for specific hillslope investigations, in terms of data production times, and spatial scales. The main purpose of the study is examining and assessing the initiation of gully erosion, identifying the sites likely to be affected by gully erosion and analyzing the conditions that favor their occurrence in the study area. The task was done comparing different gully erosion maps based on the use of an erosion index and of gully topographic thresholds. The model's outcome provides insights on the triggering mechanisms of gully processes, particularly the geomorphological conditions leading to gully initiation enhancing the capability to forecast the spatial and temporal incidence of gully erosion.

2.1 CASE STUDY AND DATA USED

The study area is the Ghapan-Olya watershed (location: 37″ 36′ 30˚ to 37″ 38′ 30˚ N and 55° 39′ 30˚ to 55° 41′ 30˚ E; Fig. 1), located in Golestan Province with an area of 520 ha. Altitude ranges from 211m and 549m, with an average slope of 26%. 30 soil samples were gathered from locations with and without gullies, and they were analyzed in the soil laboratory. The methods for estimating soil properties can be found in Kariminejad et al. (2020). In the study, three CN scenarios were estimated considering soil and land use and cover conditions in three seasons, with CN1 corresponding to the highest vegetation covers in spring, CN2 to the middle vegetation covers in summer, and CN3 to the lowest vegetation cover in winter. Land use and cover maps to calculate the CNs for the three scenarios were derived from the UAVs imagery. We considered different daily rainfall values corresponding respectively to 40, 60, and 80 mm. In the study area, there are rangelands (366 ha), agricultural lands (127 ha), and residential (27 ha). The dominant soil texture belongs to the "silt loam” class. Land use types consist of rangelands and agricultural lands, and residential with percentages of 74.09, 21.43, and 4.48, respectively. Figure 2 shows examples of known gullies’ location in the study area. Accurate spatial gully characterization was derived through the UAV based photogrammetric acquisition. We mapped gully location using the orthophoto image derived from UAV in ArcGIS 10.8. In the analysis the locations without gullies were selected in places without any spectral and morphometric gully evidence.

Afixed-wing UAVs (Sensefly eBee x) was used with the camera model Sensefly Aeria X with a focal length of 18.5 mm. Acquisitions were planned to guarantee an average Ground Sampling Distance of 5 cm. The final resolution of the orthomosaic is 5 cm/pixel. The planning was done by a trained UAV operator supported by the senseFly eMotion flight planning software. A GPS RTK correction was also added. The flight paths were designed to have a side overlap of 70% and front overlap of 85%. The average height for the flight was 220 m above the surface. Aerial images acquired during the survey were processed with the Pix4Dmapper photogrammetry software.

2.2 METHODS

In this study, the LANDPLANER model was used to model gully processes. As described by Rossi (2014), the model exploits two different approaches to model the gully occurrence, based respectively on the use of a dynamic erosion index estimation (i.e., varying dynamically with rainfall and in turn with surface runoff along with geomorphometrical and curve number data) and of a static topographic gully threshold (i.e., varying solely with geomorphometrical and curve number data). LANDPLANER holds on the curve number (CN) concept to estimate the surface runoff in a distributed manner. The model requires as main inputs a slope, drainage direction and accumulation maps easily derivable in GIS environment from a DEM (Fig. 4), and a curve number map (Fig. 5) that can be derived from soil, vegetation and land use and cover information following the methodology described in USDA NCRS (2012).

Once the runoff of each cell in the study are calculated for a given rainfall, the corresponding erosion index values are estimated using the following equation (see Chap. 3 in Rossi, 2014 for details).

$${e}_{pot}=\alpha .{\left(\frac{{Q}_{off.\text{sin}\left(S\right)}}{{S}_{0.05}}\right)}^{\beta }$$ (1)

Where Q_off and S are respectively the runoff for each cell in mm and the slope angle. The coefficients β and α are generally assumed with a unitary value but for accurate quantitative erosion index estimation a calibration to quantify the two coefficients based on observed rainfall, runoff and erosion data series is required. S_0.05 in mm is estimated from the curve number values using the following equation:

$${S}_{0.05}= {25.4 1.33\left[\frac{1000}{CN}-10\right]}^{1.15}$$ (2)

Differently from the erosion index that is dynamic in nature, the second approach to estimate gully occurrence is static and based on concept of topographic gully threshold (Torri & Poesen, 2014, Rossi et al., 2015; Torri et al., 2018) expressed by the following equation:

$$\text{s}\text{i}\text{n}\left(S\right)\ge {0.73ce}^{1.3RFC}(0.00124.{S}_{0.05}-0.037){A}^{-0.38}$$ (3)

In the equation S_0.05 is calculated with Eq. 2, s is the slope angle, c is a constant equal to 1, RFC is the rock fragment content and A is the upslope contributing area in ha.

Different operational modes are incorporated in LANDPLANER. The software allows to select/unselect model components. This is particularly important to tailor the software application to different study area condition, but also to permit diversified verification and validation analysis. Moreover, the model allows specifying the boundary condition of the infiltration model, and using rainfall sequences either as single value for the entire area, or as a distributed rainfall map. When using rainfall sequences in both the previous cases, the infiltration procedure is performed for each value (or map) of rainfall in the sequence. Rainfall can be a real rainfall data series, or as series of values corresponding to different rainfall event scenarios.

LANDPLANER simulations allows estimating different outputs. For a given combination of CN and rainfall scenario, the software outputs in .asc format include runoff (run-on, runoff, peak runoff), infiltration (soil water contents, water exceedance in the horizons), evaporation (plant transpiration, soil evaporation) and erosion (erosion starting and ending points, erosion channel identification, erosion deposition indices) maps.

2.3 VALIDATION APPROACH

Validating a transient model requires a multifold strategy. Indeed, both the infiltration and the gully model need to be validated, requiring detailed information on (i) the gully spatial occurrence, (ii) the soil water saturation and circulation, and (iii) the hydrological and geotechnical soil characteristics. These data are difficult to obtain, particularly for areas larger than single hillslopes. Moreover, data's spatial uncertainty and representativeness pose additional complexities that need to be managed to obtain meaningful validation results.

This paper exploits a similar approach to validate the LANDPLANER landslide model. The concept of the fourfold plot evolves from the deliberations given in Lombardo and Mai (2018), where previous studies have illustrated that the possibility of cutoffs among unstable and stable situations is data-reliant (Mergili et al. 2014; Raia et al., 2014; Kariminejad et al., 2020). Box plots were also used to display the five-quantiles summary statistics of a set of data. Specifically, they were used for the erosion index validation such as the fourlfold/contingency table statistics and plots for the topographic threshold outputs. The other validation measures include sensitivity, specificity, fallout, precision and recall. The contingency table (true negative, true positive, false negative, false positive) was used to evaluate the capability of the model. These functions are as follows:

$$\operatorname{Re} call=\frac{{TP}}{P}=\frac{{TP}}{{TP+FN}}$$ (7)

$$F - measure=2 \times \frac{{P \times \operatorname{Re} call}}{{P+\operatorname{Re} call}}$$ (8)

$$Fallout=\frac{{FP}}{{(TP+FP)}}$$ (9)

$$TPR(sensitivity;recall)=\frac{{TP}}{P}=\frac{{TP}}{{TP+FN}}$$ (10)

$$TNR \left(specificity\right)= \frac{TN}{TN+FP}$$ (11)

TPR explains the proportion of locations with observed gully correctly predicted and TNR shows the proportion of locations without gullies correctly predicted that are meaningful statistics. Box plots and fourlfold/contingency table statistics and plots were derived comparing the models’ spatial prediction and the maps of locations with or without gullies derived by analyzing UAV photogrammetric imagery.

In this study, the presence/absence of gullies in relationship to the predisposing variables was characterized, and then LANDPLANER was applied with different input datasets and different input scenarios, including curve numbers and rainfall, to model the spatial distribution of gully locations. The output of the altitude analysis illustrated that the density of gully locations is more frequent in regions with smaller elevations, approximately 200-300m (Fig. 6A). Whereas, regions with no gully erosion have greater values of altitude (400-500m). Regarding the plot of slope degree (Fig. 6B), gully erosions are mostly distributed in steeper slopes with the generality of values ranging between 15 to 30 degrees.

Similarly, the locations without gullies occur on slopes with the generality between 10 and 15. The spatial differences in the values of the accumulation density (Fig. 6C) corresponding to locations with and without gullies is less pronounced with respect to slope. However, they overall represent greater values of average accumulation density for the regions with gully erosion. Considering the drainage direction distribution (Fig. 6D), gullies are preferentially occurring in south-east facing slope, though regions without gully erosion are more routinely facing north-west.

Figure 6 shows the erosional values simulated for the three main CN scenarios (CN1, CN2, CN3) and the three main daily rainfall scenarios (40, 60, 80). In particular, Fig. 7A shows the allocations of CN values in conjunction with the presence or absence of gully. In contrast, Fig. 7B, C, and D for the same locations show alterations of the erosion index estimated for all six CN and daily rainfall scenarios. Figure 7A confirms that locations without gullies are often in conjunction with the CN value in spring. Figure 7B-D shows that the greater values of erosion index are estimated for the CN3 and the average of 80 mm daily rainfall, with the greatest dissimilarity between the values for the regions with and without gully erosion (0.0742 − 0.0098 = 0.0644).

TErosion index results referred to the CN1, CN2, and CN3, and rainfall 40, 60, and 80 are shown in Fig. 8A-I. The greater values of soil erosion indices were obtained by for the CN3 scenario (autumn) when the daily rainfall is 80 mm (6.27), continued by the summer scenario (CN2) with 80 mm daily rainfall (4.88), and spring (CN1) again with 80 mm daily rainfall (2.99). In addition, topographic threshold maps illustrate the greatest amount of soil erosion for the CN scenario (without considering daily rainfall) in autumn (Fig. 9 (A-B)).

Finally, the model results obtained by applying the topographic threshold (ESP) for three CN scenarios (Fig. 10) were analysed by applying fourfold plots that graphically compare the observed locations with/without gully erosion with the ESP predictions. Other validation processes were obtained using the CN1 scenario that had enough capability to correctly classify (Fig. 10A) 90 gully locations (TP value) and 3 locations without gully (TN value) with only the lowest number of the locations of gully erosion and no gully erosion (88 + 1 = 89) incorrectly predicted (FN + FP) (Table 1).

Table 1

Statistical indeces derived fotmt the contingency table obtained by applying the topographic threshold (ESP) model for three CN scenarios.
ESP_scenario	TPR	FPR	Recall	F-measures	Fallout	Specificity	Sensitivity
Spring (CN1)	0.999	0.995	0.999	0.757	0.391	0.999	0.005
Summer (CN2)	0.977	0.958	0.977	0.754	0.387	0.977	0.042
Autumn (CN3)	0.922	0.895	0.922	0.734	0.390	0.922	0.105

The empirical relationships derived in the literature linking rainfall event occurrence and land use to the surface runoff features are often too simplified. There is the need of more comprehensive tools able to account the inherent complex nature of erosion, which involves a series of processes (such as hydraulic erosion, sediment erosion, tunnelling, folding, pressure cracking, and mass movement processes, including slip) and their relations with the hillslope and soil dynamics. The integrated effects of these processes eventually lead to some degree of erosion and its nonlinear growth over the long term (Van Val Agham et al., 2005). The process-oriented approach has not been considered for all types of erosion. Accordingly, there is a pressing need to use physical models to deal with different erosional, hydraulic, and hydrological processes, such as tunnelling and landslides for a better control (Campo-Biscose et al., 2013; Tori and Poznan, 2014).

The present study allowed for the evaluation of gully erosion and expansion, a major erosion process, using physically related (i.e., erosion index) and conceptual (i.e., topographic threshold) approaches. In particular, the erosion index explicitly relates the erosion with runoff for a rainfall event using a distributed routing procedure and with other factors controlling the flow velocity (i.e., slope) and land use and soil physical conditions (i.e., derived from CN). In addition, also the gully head topographic threshold (Torri & Poesen, 2014; Torri et al., 2018; Rossi et al., 2015) concept holds a physical basis (Rossi et al., 2022). Thus, the selected model, even if simplified, allowed for a physical evaluation of the erosion occurrence, unlike other black box, empirical, conceptual, or regression models etc. This potentially enables the simulation of future events and their evaluation in terms of processes and physical principles.

Using LANDPLANER and exploiting the minimum, maximum and the reference curve number seasonal scenarios multiple simulations to estimate the erosion index and topographic threshold value in the studied watershed were run. As expected, the results (Fig. 7 to 10) shows that the gully occurrence is controlled by rainfall and land use and in turn the landscape shaping and development. Agricultural land was confirmed to be the most affected land use by soil erosion (Torri et al., 2018).

The simulated scenarios 1 to 3 used three daily rainfall intensities corresponding to 40, 60, and 80 mm/day. In the scenario 1, where the larger CN (a less permeable condition in dark red) are close to the morphological divide and the lower CN value (higher permeability) is close to the basin outlet (in green), the larger peak runoff values for both rainfall intensities are observed close to the basin divides. In contrast, in scenario 3, the results are the opposite. The different simulated rainfall intensities produce significantly different peak runoff values with their distribution in the basin strictly controlled by the land use/soil distribution. The hydrological connection inside the basin is possible only for higher rainfall intensities, but the land use/soil distribution firmly controls its geographical distribution. The larger the area, the less probable it is to reach the total connection of the basin. The more permeable conditions (lower CN in green) effectively reduce the amount of runoff reaching the basin outlet. The most critical condition for the gully formation is observed for scenario 3. All these results show that the gully triggering conditions, and potential gully formation locations, are strictly controlled by the combination of the land use/soil distribution and rainfall intensity.

Estimating gully occurrence in relation with topographical variables (e.g.., slope) unravels the portion of the study area more affected by gully erosion. The results obtained in this study are in line with previous research (Valentin et al., 2005; Lucà et al., 2011; Frankl et al., 2012; Bernatek-Jakiel et al., 2016). In comparison with the recent study carried out by Kariminejad et al., 2020, gully erosion density is nearly in the same slope aspect (south-east facing slopes) and the same elevation (around 200–300 m), but in gentler slope (within 15–30°), and for lower average accumulation value (< 100 m) (Fig. 6). They also explained that on average, the higher the CN, the higher the runoff, the higher the erosion index, and the larger should be the places predicted with gully erosion based on the topographic threshold approach. Here, the model simply predicts a larger (lower) gully occurrence depending on the higher (lower) CN values, possibly highlighting the evaluation of temporal gully occurrence variation. Hosseinalizadeh et al (2020) stated that gully erosion is common in different pedo-climatic regions of Iran with slope range of 2–27%. Higher values of topographic threshold and erosion index are also observed for the cold season in winter, where rainfall is highest and vegetation cover is lowest in the rangelands. This can also be extended to different types of erosion and even in determining the potential of the area for landslide (Hong and Adler, 2007; Liao et al., 2010; Liao et al., 2012).

Being the anthropic influence one of the main drivers of gully erosion occurrence, conventional agriculture should be replaced by conservation agriculture to reduce agricultural practices impact and machinery. In this case, soil organic matter and soil biodiversity should be increased. In modeling terms, this will correspond to a reduction of CN mainly due to the presence of permanent land cover but also to the induced soil properties change, with the primary effect of reducing runoff and hence surface erosion. This highlights also how gully erosion and improve soil health can be managed by applying a wise and shrewd land management. Besides, the possible increase of intense rainfall events due to climate changes, should potentially affect the erosion occurrence in the coming decades.

The LANDPLANER model, allowed a comprehensive comparison of the watershed response to environmental changes. This model has been successfully used elsewhere such as in Italy (Rossi et al., 2015; Agostini et al., 2022) to investigate erosion occurrence as a result of different landscape, climatic and vegetation changes. LANDPLANER can estimate the erosion process with some limitations in terms of model assumptions (i.e., using a rainfall event-based approach; using simplified erosion indices). Further work needs to be done to establish where and whether the model assumptions are appropriate and to verify the model applicability in different environmental conditions. Being open source, the software allows to modify or add aspects of the hillslope dynamics and integrate them in the modelling framework. Remarkably, the integration of the dynamic modification of the slope morphology and its effects on the possible related hillslope processes would enhance the model's potential in characterizing their dynamical mutual relations.

Gully detection and modelling are methodologically challenging (Grellier et al., 2012). Scientists need to cope with the problem exploiting adequate data and appropriate applicable methods (i.e., geophysical, geologic, pedological). for assessing hazards posed by gully erosion and for predicting their occurrence. This will be an essential knowledge both for land management and for the implementation of reliable early warning systems aimed at predicting erosion phenomena.

The results obtained in this study, suggest that specific conservation practices should be carried out in order to prevent the concentration of runoff from upstream areas leading to piping and gullying. Biological operations including minimum mechanical soil disturbance, permanent soil organic cover, and varied crop sequences (CN scenario) may be adopted to control gully occurrence or development.

This study investigated the channelized erosion occurrence in an area in Iran in the last decade using the LANDPLANER model, hydrodynamic simulations, andhigh resolution DEM derived from the UAV. The scarcity of detailed and temporal knowledge of all the processes and parameters limited the applicability of more complex physically based models, however the selected model proved to be effective to analyze gully erosion in the area and the basics of this physical processes. Our results specifically shows that the gully triggering conditions and potential gully formation locations are strictly controlled by the spatial distribution of soil texture/land use, the average amount of daily rainfall events, and their combination. Despite being simplified, LANDPLANER proved to be an effective gully forecasting tool over a small region with low number of inputs and high-quality data and helped to quantify the effect of seasonal agricultural practices on gully occurrence providing meaningful indication for a correct land management

ACKNOWLEDGMENT

This study was supported by Iran National Science Foundation (INSF) (Grant No. 99011991).

Compliance with Ethical Standards

The authors declare that they have no conflict of interest.

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Evaluating land degradation by gully erosion through soil erosion indices and rainfall thresholds

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Abstract

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1 Introduction

2 Material And Methods

2.1 CASE STUDY AND DATA USED

2.2 METHODS

2.3 VALIDATION APPROACH

4 Results

5 Discussions

6 Conclusions

Declarations

References

Status:

Journal Publication

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