scholarly journals Soil erosion risk assessment at small catchments scale: comparison of GIS-based modelling and field survey data and its implication for environmental maintenance of rivers

2015 ◽  
Vol 2 ◽  
pp. 289
Author(s):  
Juris Soms
Keyword(s):  
Risk Assessment ◽  
Soil Erosion ◽  
Soil Loss ◽  
Field Survey ◽  
Spatial Scales ◽  
Theoretical Data ◽  
Economic Effects ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Small Catchments

One of the limitations to implementation of effective measures to mitigate negative environmental and economic effects associated with soil erosion is the lack of data on the geographic distribution of erosion risk and potential erosion hotspots. Hence, experts and policy makers in many cases have no spatially referenced information on which to base their decisions. There is a trend approved by EU institutions and agencies to use soil erosion models which can be integrated into geographic information systems (GIS) environment in order to obtain data at different spatial scales and to assist such decision-making. Despite that, until now in Latvia only some studies on the GIS-based modelling of potential soil losses have been conducted. Considering that, in the study presented in this paper soil erosion risk assessment has been performed by the widely used Revised Universal Soil Loss Equation (RUSLE) model over five selected small catchments of the river Daugava valley. In order to validate the results of modelling and to assess if theory accords with a real situation, the theoretical data were compared with information gained from the field survey of the same catchments. Modelled potential soil loss from each of five catchments under study totals 0.25; 0.26; 0.42; 0.51 and 0.58 t ha<sup>-1</sup> y<sup>-1</sup> in average. However, results of the comparison indicate the discrepancies between modelled and measured values, i.e. the used empirical model underestimates the soil erosion risk. The recognition of this fact raises implication for appropriate environmental maintenance of rivers, due to possible underestimation of eroded material delivery to receiving streams and, subsequently, under-prediction of water pollution.

scholarly journals Terrain Characterization and Soil Erosion Risk Assessment for Watershed Prioritization Using Remote Sensing and GIS (A case study of Nawagaon Maskara Rao Watershed, Saharanpur, India)

2009 ◽  
Vol 23 (1) ◽  
pp. 86
Author(s):  
Beny Harjadi
Keyword(s):  
Remote Sensing ◽  
Risk Assessment ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Conservation ◽  
Soil Loss ◽  
Remote Sensing And Gis ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Conservation Measures

Soil erosion is crucial problem in India where more than 70% of land in degraded. This study is to establish conservation priorities of the sub watersheds across the entire terrain, and suggest suitable conservation measures. Soil conservation practices are not only from erosion data both qualitative SES (Soil Erosion Status) model and quantitative MMF (Morgan, Morgan and Finney) model erosion, but we have to consider LCC (Land Capability Classification) and LULC (Land Use Land Cover). Study demonstrated the use of RS (Remote Sensing) and GIS (Geographic Information System) in soil erosion risk assessment by deriving soil and vegetation parameters in the erosion models. Sub-watersheds were prioritized based on average soil loss and the area falls under various erosion risk classes for conservation planning. The annual rate of soil loss based on MMF model was classified into five soil erosion risk classes for soil conservation measures. From 11 sub watersheds, for the first priority of the watershed is catchment with the small area and the steep slope. Recommendation for steep areas (classes VI, VII, and VIII) land use allocation should be made to maintain forest functions.

scholarly journals Predicting the Impact of Future Land Use and Climate Change on Potential Soil Erosion Risk in an Urban District of the Harare Metropolitan Province, Zimbabwe

2021 ◽  
Vol 13 (21) ◽  
pp. 4360
Author(s):  
Andrew K. Marondedze ◽  
Brigitta Schütt
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Rainfall Erosivity ◽  
Climate Scenarios ◽  
Residential Areas ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Loss Rates

Monitoring urban area expansion through multispectral remotely sensed data and other geomatics techniques is fundamental for sustainable urban planning. Forecasting of future land use land cover (LULC) change for the years 2034 and 2050 was performed using the Cellular Automata Markov model for the current fast-growing Epworth district of the Harare Metropolitan Province, Zimbabwe. The stochastic CA–Markov modelling procedure validation yielded kappa statistics above 80%, ascertaining good agreement. The spatial distribution of the LULC classes CBD/Industrial area, water and irrigated croplands as projected for 2034 and 2050 show slight notable changes. For projected scenarios in 2034 and 2050, low–medium-density residential areas are predicted to increase from 11.1 km2 to 12.3 km2 between 2018 and 2050. Similarly, high-density residential areas are predicted to increase from 18.6 km2 to 22.4 km2 between 2018 and 2050. Assessment of the effects of future climate change on potential soil erosion risk for Epworth district were undertaken by applying the representative concentration pathways (RCP4.5 and RCP8.5) climate scenarios, and model ensemble averages from multiple general circulation models (GCMs) were used to derive the rainfall erosivity factor for the RUSLE model. Average soil loss rates for both climate scenarios, RCP4.5 and RCP8.5, were predicted to be high in 2034 due to the large spatial area extent of croplands and disturbed green spaces exposed to soil erosion processes, therefore increasing potential soil erosion risk, with RCP4.5 having more impact than RCP8.5 due to a higher applied rainfall erosivity. For 2050, the predicted wide area average soil loss rates declined for both climate scenarios RCP4.5 and RCP8.5, following the predicted decline in rainfall erosivity and vulnerable areas that are erodible. Overall, high potential soil erosion risk was predicted along the flanks of the drainage network for both RCP4.5 and RCP8.5 climate scenarios in 2050.

scholarly journals Erosion Modelling In A Mediterranean Subcatchment Under Climate Change Scenarios Using Pan-European Soil Erosion Risk Assessment (PESERA)

2015 ◽  
Vol XL-7/W3 ◽  
pp. 359-365 ◽  
Author(s):  
A. Cilek ◽  
S. Berberoglu ◽  
M. Kirkby ◽  
B. Irvine ◽  
C. Donmez ◽  
...  
Keyword(s):  
Climate Change ◽  
Risk Assessment ◽  
Land Use ◽  
Soil Erosion ◽  
Mediterranean Region ◽  
Geographical Information ◽  
Climate Change Scenarios ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
The Mediterranean

The Mediterranean region is particularly prone to erosion. This is because it is subject to long dry periods followed by heavy bursts of erosive rainfall, falling on steep slopes with fragile soils, resulting in considerable amounts of erosion. In parts of the Mediterranean region, erosion has reached a stage of irreversibility and in some places erosion has practically ceased because there is no more soil left. With a very slow rate of soil formation, any soil loss of more than 1 t ha<sup>−1</sup> yr<sup>−1</sup> can be considered as irreversible within a time span of 50-100 years. The objectives of this study were i) to estimate the temporal and spatial distribution of soil erosion under climate change scenarios in study area ii) to assess the hydrological runoff processes. <br><br> In this study, climate data, land use, topographic and physiographic properties were assembled for Egribuk Subcatchment at Seyhan River Basin in Turkey and used in a process-based Geographical Information System (GIS) to determine the hydrological sediment potential and quantify reservoir sedimentation. The estimated amount of sediment transported downstream is potentially large based on hydrological runoff processes using the Pan-European Soil Erosion Risk Assessment (PESERA) model. The detailed model inputs included 128 variables derived mainly from, soil, climate, land use/cover, topography data sets. The outcomes of this research were spatial and temporal distribution of erosion amount in t ha<sup>−1</sup> yr<sup>−1</sup> or month<sup>−1</sup>.

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