Evolutionary Prediction of Soil Loss from Observed Rainstorm Parameters in an Erosion Watershed Using Genetic Programming

Various environmental problems such as soil degradation and landform evolutions are initiated by a natural process known as soil erosion. Aggregated soil surfaces are dispersed through the impact of raindrop and its associated parameters, which were considered in this present work as function of soil loss. In an attempt to monitor environmental degradation due to the impact of raindrop and its associated factors, this work has employed the learning abilities of genetic programming (GP) to predict soil loss deploying rainfall amount, kinetic energy, rainfall intensity, gully head advance, soil detachment, factored soil detachment, runoff, and runoff rate database collected over a three-year period as predictors. Three evolutionary trials were executed, and three models were presented considering different permutations of the predictors. The performance evaluation of the three models showed that trial 3 with the highest parametric permutation, i.e., that included the influence of all the studied parameters showed the least error of 0.1 and the maximum coefficient of determination (R2) of 0.97 and as such is the most efficient, robust, and applicable GP model to predict the soil loss value.

Quantifying the impacts of urban gullying at the scale of the Democratic Republic of Congo

<p>Urban gullies cause major infrastructural damages and often claim casualties in many tropical cities of the Global South. Nonetheless, our understanding of this hazard currently remains limited to some case studies, while the impacts at larger scales remain poorly quantified. Here, we aim to bridge this gap by making a first assessment of the number of persons and buildings affected by urban gullies at the scale of the Democratic Republic of Congo (DRC). We used Google Earth imagery in combination with local news sources and earlier research to identify 25 cities in DRC where urban gullies occur at a significant scale (at least ten urban gullies). This list is likely exhaustive. Next, for each of these cities, we used Google Earth and other high resolution images to map all visible urban gullies, evaluate their expansion rate and inventorize detectable damages to houses and roads. In total, more than two thousand urban gullies were mapped across the 25 affected cities.  Overall, the problem of urban gullies in DRC is especially acute in the cities of Kinshasa, Mbujimayi, Tshikapa, Kananga, Kabinda, and Kikwit. Over 80% of these gullies were active during the observation period (typically from 2002 to 2020). We identified 4257 houses and 998 roads that were destroyed because of the formation and expansion of urban gullies. Nonetheless, the actual impacts are likely much larger since the limited amount of imagery available does not allow quantifying all impacts. For example, in most cases, a large urban gully was already present on the earliest image available.</p><p>We also made an estimate of the total number of persons that are directly affected, as well as the number of persons currently at risk. Using high resolution estimates of population density and taking into account the current position of urban gullies, we estimate that a total of 133000 people have already lost their house due to formation and expansion of urban gullies. Given that these gullies are typically less than 30-years old, we estimate that on average, at least 4000 people/year lose their house as a result of urban gullies in DRC. This may still be an underestimation. By considering the population that lives in the direct vicinity (<100 m) of an urban gully, we estimate that around 1.2 million people in D.R. Congo are currently at risk and/or experience significant impacts because of urban gullies (e.g. reduced land value, problems with trafficability, stress). An estimated 449000 persons live less than 100 m away from a gully head (which is typically the most active part of the gully) and are therefore likely at high risk to be significantly affected by urban gullies in the coming years.</p><p>Overall, this research shows that urban gullying is a very serious problem in the DRC, but likely also in many other tropical countries. More research is needed to better understand this processes and, ultimately, to prevent and mitigate its impacts. The results and the database of this study provide an important step towards this.</p>

Can we use the Curve Number approach to predict gully head occurrence at the continental scale of Africa?

<p>Gully erosion is an important land degradation process, threatening soil and water resources worldwide. However, in contrast to sheet and rill erosion, our ability to simulate and predict gully erosion remains limited, especially at the continental scale. Nevertheless, such models are essential for the development of suitable land management strategies, but also to better quantify the role of gully erosion in continental sediment budgets. We aim to bridge this gap by developing a first spatially explicit and process-oriented model that simulates average gully erosion rates at the continental scale of Africa.</p><p>We are developing a model that predicts the likelihood of gully head occurrence by means of the Curve Number (CN) method. This model will allow to simulate the spatial patterns of gully density at high resolution (30m) based on the physical principles that control the gully erosion process by using GIS and spatial data sources that are available at the continental scale. To calibrate and validate this model, we make use of an extensive database of 44 000 gully heads mapped over 1680 sites that are randomly distributed across Africa. The exact location of all gully heads was manually mapped by trained experts, using high resolution optical imagery available in Google Earth. This allows to extract very detailed information at the level of the gully head, such as the local slope and the area draining to the gully.</p><p>Based on an explorative analysis on a subset of this dataset we found that the CN method does not directly allow to make reliable predictions on gully head occurrence within a pixel. Although land use and land cover seem to play an important role (with gully heads being clearly located in erosion-prone land use classes), the hydrological soil groups (HSGs) based on soil texture do not provide a clear relation between soils with high runoff risk and gully occurrence. A potential cause for this is likely that compensating soil effects occur: i.e. HSGs that produce low runoff volumes may be characterized by a lower soil cohesion, making them nonetheless prone to gullying. This may then cause the combination of HSG and land use to be an insignificant predictor of gully occurrence. Also uncertainties on the input data likely play an important role in this.</p><p>Overall, our results indicate that modelling gully densities using a process-oriented and spatially explicit method offers opportunities to better quantify this important land degradation process at the global scale. Nevertheless, a key challenge lies in accurately quantifying the importance of soil characteristics and especially in better understanding their relative contribution to runoff production and soil cohesion.</p>

Changes in gully sizes: the role of interactions between land-use changes and other driving factors

<p>Changes in gully sizes are brought about by the interactions among gully-driving factors. The aim of this paper is to understand how interactions among land-use changes and other gully-drivers: relative relief, maximum slope, proximity to rivers and roads influence changes in gully length and gullied area. The study area covers 535 km<sup>2</sup> in the Orlu region of southeast Nigeria. Gully heads were mapped using high resolution data (0.61 – 5m) acquired in November 2009 and December 2018 while supervised land-use classification was undertaken for both years. Three land-use classes were identified: non-vegetated, open vegetation and fallow. Geomorphic variables were acquired from the 30 m SRTM-DEM while gully head distances from rivers and roads were calculated using the distance tool in ArcGIS. Two sets of multiple regression analyses were undertaken, first to understand effects of land-use changes and secondly to ascertain influence of the other driving factors on changes in gully sizes. Non-vegetated surfaces increased from 58.6 km<sup>2</sup> to 144.7 km<sup>2</sup> between 2009 and 2018, while reduction in fallowed lands from 281.2 km<sup>2</sup> to 57.8 km<sup>2</sup> was observed. Of the 58.6 km<sup>2</sup> of non-vegetated lands in 2009, 10.9 km<sup>2</sup> were converted to open vegetation, while 0.18 km<sup>2</sup> was transformed to fallow in 2018, 50.9 km<sup>2</sup> of fallow-cover remained the same between 2009 and 2018 while 29 km<sup>2</sup> were converted to non-vegetated and 201.3 km<sup>2</sup> were used for open vegetation in 2018. These land use changes will likely increase volume of surface runoff.  Gully numbers grew from 26 to 39, mean gully length increased from 0.26 to 0.43 km which translates to a mean headward retreat of 17 m yr<sup>-1</sup>. Total length of all gullies changed from 10.22 to 16.63 km. Mean gullied area increased from 13775 to 16183 m<sup>2</sup>, indicating an areal retreat of 241 m<sup>2</sup> yr<sup>-1</sup>, total gullied area grew from 0.36 km<sup>2 </sup>to 0.62 km<sup>2</sup>. Relative relief ranged between 6 – 46 m, lands around the rivers had the highest concentration of gullies, and there was a sharp rise in slope from 0 – 58.2% within a distance less than 500 m from the rivers. The first Multiple regression result indicated that associations between changes in gully length, non-vegetated and fallow land-use classes were significant at 0.05. Results of the second multiple regression analysis showed that only gully head distance from rivers had a significant positive effect on changes in gullied area. Bearing in mind the configuration of the land and rise in slope from rivers, increased volume of surface runoff (caused by changes in land use and higher slope rise) can attain higher erosive power as it approaches the river. This increased surface flow passing through gully channels on its way to the river, can enhance gully length and areal retreat.</p><p><strong>Keywords: Gully erosion, land-use changes, gully-drivers, south east Nigeria</strong></p>

A New Method to Predict Gully Head Erosion in the Loess Plateau of China Based on SBAS-InSAR

Gully head erosion causes serious land degradation in semiarid regions. The existing studies on gully head erosion are mainly based on measuring the gully volume in small-scale catchments, which is a labor-intensive and time-consuming approach. Therefore, it is necessary to explore an accurate method quantitatively over large areas and long periods. The objective of this study was to develop a model to assess gully head erosion in the Loess Plateau of China using a method based on the SBAS-InSAR technique. The gully heads were extracted from the digital elevation model and validated by field investigation and aerial images. The surface deformation was estimated with SBAS-InSAR and 22 descending ALOS PALSAR datasets from 2007 to 2011. A gully head erosion model was developed; this model can incorporate terrain factors and soil types, as well as provides erosion rate predictions consistent with the SBAS-InSAR measurements (R2 = 0.889). The results show that gully head erosion significantly depends on the slope angle above the gully head, slope length, topographic wetness index, and catchment area. The relationship between these factors and the gully head erosion rate is a power function, and the average rate of gully head erosion is 7.5 m3/m2/year, indicating the high erosional vulnerability of the area. The accuracy of the model can be further improved by considering other factors, such as the stream power factor, curvature, and slope aspect. This study indicates that the erosion rate of gully heads is almost unaffected by soil type in the research area. An advantage of this model is that the gully head area and surface deformation can be easily extracted and measured from satellite images, which is effective for assessing gully head erosion at a large scale in combination with SBAS-InSAR results and terrain attributes.