Comparison of the Spatial Wind Erosion Patterns of Erosion Risk Mapping and Quantitative Modeling in Eastern Austria

Various large-scale risk maps show that the eastern part of Austria, in particular the Pannonian Basin, is one of the regions in Europe most vulnerable to wind erosion. However, comprehensive assessments of the severity and the extent of wind erosion risk are still lacking for this region. This study aimed to prove the results of large-scale maps by developing high-resolution maps of wind erosion risk for the target area. For this, we applied a qualitative soil erosion assessment (DIN 19706) with lower data requirements and a more data-demanding revised wind erosion equation (RWEQ) within a GIS application to evaluate the process of assessing wind erosion risk. Both models defined similar risk areas, although the assignment of severity classes differed. Most agricultural fields in the study area were classified as not at risk to wind erosion (DIN 19706), whereas the mean annual soil loss rate modeled by RWEQ was 3.7 t ha−1 yr−1. August was the month with the highest modeled soil loss (average of 0.49 t ha−1 month−1), due to a low percentage of vegetation cover and a relatively high weather factor combining wind speed and soil moisture effects. Based on the results, DIN 19706 is suitable for a general classification of wind erosion-prone areas, while RWEQ can derive additional information such as seasonal distribution and soil loss rates besides the spatial extents of wind erosion.

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Changes in wind erosion climatic erosivity in northern China from 1981-2016: a comparison of two climate/weather factors of wind erosion models

Climate Research ◽

10.3354/cr01640 ◽

2021 ◽

Vol 83 ◽

pp. 133-146

Author(s):

F Zhang ◽

J Wang ◽

X Zou ◽

R Mao ◽

DY Gong ◽

...

Keyword(s):

Wind Erosion ◽

Qaidam Basin ◽

Dust Emission ◽

Northern China ◽

Northwest China ◽

Gobi Desert ◽

Weather Factors ◽

Weather Factor ◽

Increasing Trend ◽

Erosion Equation

Wind erosion is largely determined by wind erosion climatic erosivity. In this study, we examined changes in wind erosion climatic erosivity during 4 seasons across northern China from 1981-2016 using 2 models: the wind erosion climatic erosivity of the Wind Erosion Equation (WEQ) model and the weather factor from the Revised Wind Erosion Equation (RWEQ) model. Results showed that wind erosion climatic erosivity derived from the 2 models was highest in spring and lowest in winter with high values over the Kumtag Desert, the Qaidam Basin, the boundary between Mongolia and China, and the Hulunbuir Sandy Land. In spring and summer, wind erosion climatic erosivity showed decreasing trends in whole of northern China from 1981-2016, whereas there was an increasing trend in wind erosion climatic erosivity over the Gobi Desert from 1992-2011. For the weather factor of the RWEQ model, the difference between northern Northwest China and the Gobi Desert and eastern-northern China was much larger than that of the wind erosion climatic erosivity of the WEQ model. In addition, in contrast to a decreasing trend in the weather factor of the RWEQ model over southern Northwest China during spring and summer from 1981-2016, the wind erosion climatic erosivity of the WEQ model showed a decreasing trend for 1981-1992 and an increasing trend for 1992-2011 over southern Northwest China. According to a comparison between dust emission and wind erosion climatic erosivity, the 2 models have the ability to project changes in future wind erosion in northern China.

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Detailed Analysis of Spatial–Temporal Variability of Rainfall Erosivity and Erosivity Density in the Central and Southern Pannonian Basin

Sustainability ◽

10.3390/su132313355 ◽

2021 ◽

Vol 13 (23) ◽

pp. 13355

Author(s):

Tanja Micić Ponjiger ◽

Tin Lukić ◽

Biljana Basarin ◽

Maja Jokić ◽

Robert L. Wilby ◽

...

Keyword(s):

Temporal Variability ◽

Large Scale ◽

Pannonian Basin ◽

Temporal Trends ◽

Mitigation Measures ◽

Annual Rainfall ◽

Rainfall Erosivity ◽

Erosion Risk ◽

Erosion Processes ◽

Starting Point

Estimation of rainfall erosivity (RE) and erosivity density (ED) is essential for understanding the complex relationships between hydrological and soil erosion processes. The main objective of this study is to assess the spatial–temporal trends and variability of the RE and ED in the central and southern Pannonian Basin by using station observations and gridded datasets. To assess RE and ED, precipitation data for 14 meteorological stations, 225 grid points. and an erosion model consisting of daily, monthly, seasonal, and annual rainfall for the period of 1961–2014 were used. Annual RE and ED based on station data match spatially variable patterns of precipitation, with higher values in the southwest (2100 MJ·mm·ha−1·h−1) and southeast (1650 MJ·mm·ha−1·h−1) of the study area, but minimal values in the northern part (700 MJ·mm·ha−1·h−1). On the other hand, gridded datasets display more detailed RE and ED spatial–temporal variability, with the values ranging from 250 to 2800 MJ·mm·ha−1·h−1. The identified trends are showing increasing values of RE (ranging between 0.20 and 21.17 MJ·mm·ha−1·h−1) and ED (ranging between 0.01 and 0.03 MJ·ha−1·h−1) at the annual level. This tendency is also observed for autumn RE (from 5.55 to 0.37 MJ·mm·ha−1·h−1) and ED (from 0.05 to 0.01 MJ·ha−1·h−1), as for spring RE (from 1.00 to 0.01 MJ·mm·ha−1·h−1) and ED (from 0.04 to 0.01 MJ·ha−1·h−1), due to the influence of the large-scale processes of climate variability, with North Atlantic Oscillation (NAO) being the most prominent. These increases may cause a transition to a higher erosive class in the future, thus raising concerns about this type of hydro-meteorological hazard in this part of the Pannonian Basin. The present analysis identifies seasons and places of greatest erosion risk, which is the starting point for implementing suitable mitigation measures at local to regional scales.

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Determination of Soil Endangerment by Wind Erosion with Consideration of Legislative Changes in Acceptable Soil Loss

Ekológia (Bratislava) ◽

10.1515/eko-2015-0001 ◽

2015 ◽

Vol 34 (1) ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Jozef Streďanský ◽

Lenka Lackóoá ◽

Anna Streďanská ◽

Viktor Varga

Keyword(s):

Soil Loss ◽

Wind Erosion ◽

Agricultural Land ◽

Slovak Republic ◽

Arable Land ◽

Sustainable Use ◽

Control Measures ◽

Legislative Changes ◽

Erosion Equation ◽

Erosion Intensity

AbstractValue tightening of acceptable soil loss by wind erosion in amendment to the Act No. 220/2004 on Protection and Use of Agricultural Land in the Slovak Republic from 1st of April 2013 is necessary to reconsider wind erosion intensity in agricultural territories. The paper presents results of wind erosion intensity calculation by using Wind Erosion Equation (WEQ) that is recommended by Act No. 220/2004. As observed we choose cadastral area Moèenok territory and had determined and compared changes in levels of soil endangerment of arable land by wind and spatial delamination of wind erosion in specific territory of Moèenok. According to WEQ calculation, we determined that soil loss from 3778.85 ha arable land is 1220.52 ha, which is highly endangerment by wind erosion. By defining levels of soil erosion endangerment (LSEE), we found out that area in 3rd class of endangerment rose from 1.48% to 43.37% after changing acceptable soil from 40 to 15 t ha-1 year-1. Results enable us to specify priority areas where to implement erosion control measures in according to sustainable use and protection of arable land in model area.

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Global assessment of wind erosion based on a spatially distributed RWEQ model

Progress in Physical Geography Earth and Environment ◽

10.1177/03091333211030608 ◽

2021 ◽

pp. 030913332110306

Author(s):

Guocheng Yang ◽

Ranhao Sun ◽

Yongcai Jing ◽

Muqi Xiong ◽

Jialei Li ◽

...

Keyword(s):

Soil Loss ◽

Wind Erosion ◽

Large Scale ◽

Sustainable Use ◽

Remote Sensing Data ◽

Temporal Variations ◽

Equation Model ◽

Vegetation Coverage ◽

Weather Factors ◽

Erosion Area

Wind erosion is a global environmental problem and affects the sustainable use of land soil. The current efforts in wind erosion modeling mainly focus on local scales, yet very few studies have attempted to quantify the soil losses by wind on a large scale. Here, we proposed a distributed version of the revised wind erosion equation model (DRWEQ) to assess the spatial and temporal variations of wind erosion globally. The DRWEQ model used meteorological, soil, topographic, and remote sensing data to simulate global wind erosion from 2001 to 2010. The results showed that (a) the areas of wind erosion in Africa and Asia accounted for approximately 62% of the global wind erosion area but accounted for 91% of the global total soil loss; (b) global wind erosion showed a decreasing tendency during the research period – the wind erosion with a trend of intensification occupied 40.62% of the global wind erosion area while about 59.38% of the global wind erosion area showed a weakening trend; and (c) the monthly dynamics of the wind erosion were closely correlated with the combined effects of weather factors and vegetation coverage. The soil loss rates were lower in summer and reached the peak from January to April. The method presented in this study was developed based on the tradeoff of accuracy and availability of global data, and has the potential for predicting wind erosion from regional to global scales.

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Improved Visual Localization via Graph Filtering

Journal of Imaging ◽

10.3390/jimaging7020020 ◽

2021 ◽

Vol 7 (2) ◽

pp. 20

Author(s):

Carlos Lassance ◽

Yasir Latif ◽

Ravi Garg ◽

Vincent Gripon ◽

Ian Reid

Keyword(s):

Image Retrieval ◽

Large Scale ◽

Visual Localization ◽

Localization Accuracy ◽

Additional Information ◽

Feature Representations ◽

The Mean ◽

Classical Image ◽

Vision Based Localization ◽

Improve Reliability

Vision-based localization is the problem of inferring the pose of the camera given a single image. One commonly used approach relies on image retrieval where the query input is compared against a database of localized support examples and its pose is inferred with the help of the retrieved items. This assumes that images taken from the same places consist of the same landmarks and thus would have similar feature representations. These representations can learn to be robust to different variations in capture conditions like time of the day or weather. In this work, we introduce a framework which aims at enhancing the performance of such retrieval-based localization methods. It consists in taking into account additional information available, such as GPS coordinates or temporal proximity in the acquisition of the images. More precisely, our method consists in constructing a graph based on this additional information that is later used to improve reliability of the retrieval process by filtering the feature representations of support and/or query images. We show that the proposed method is able to significantly improve the localization accuracy on two large scale datasets, as well as the mean average precision in classical image retrieval scenarios.

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Analysis of Ownership Data from Consolidated Land Threatened by Water Erosion in the Vlára Basin, Slovakia

Sustainability ◽

10.3390/su13010051 ◽

2020 ◽

Vol 13 (1) ◽

pp. 51

Author(s):

Alexandra Pagáč Mokrá ◽

Jakub Pagáč ◽

Zlatica Muchová ◽

František Petrovič

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Agricultural Land ◽

Water Erosion ◽

Land Consolidation ◽

Land Fragmentation ◽

Erosion Risk ◽

Definition Of ◽

The Relationship ◽

Soil Loss Equation

Water erosion is a phenomenon that significantly damages agricultural land. The current land fragmentation in Slovakia and the complete ambiguity of who owns it leads to a lack of responsibility to care for the land in its current condition, which could affect its sustainability in the future. The reason so much soil has eroded is obvious when looking at current land management, with large fields, a lack of windbreaks between them, and no barriers to prevent soil runoff. Land consolidation might be the solution. This paper seeks to evaluate redistributed land and, based on modeling by the Universal Soil Loss Equation (USLE) method, to assess the degree of soil erosion risk. Ownership data provided information on how many owners and what amount of area to consider, while taking into account new conditions regarding water erosion. The results indicate that 2488 plots of 1607 owners which represent 12% of the model area are still endangered by water erosion, even after the completion of the land consolidation project. The results also presented a way of evaluating the territory and aims to trigger a discussion regarding an unambiguous definition of responsibility in the relationship between owner and user.

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Water Erosion Risk Assessment in the Kenya Great Rift Valley Region

Sustainability ◽

10.3390/su13020844 ◽

2021 ◽

Vol 13 (2) ◽

pp. 844

Author(s):

George Watene ◽

Lijun Yu ◽

Yueping Nie ◽

Jianfeng Zhu ◽

Thomas Ngigi ◽

...

Keyword(s):

Rift Valley ◽

Soil Loss ◽

Large Change ◽

Risk Category ◽

Lake Nakuru ◽

Erosion Risk ◽

Erosion Rates ◽

3.0 T ◽

Great Rift Valley ◽

Loss Rates

The Kenya Great Rift Valley (KGRV) region unique landscape comprises of mountainous terrain, large valley-floor lakes, and agricultural lands bordered by extensive Arid and Semi-Arid Lands (ASALs). The East Africa (EA) region has received high amounts of rainfall in the recent past as evidenced by the rising lake levels in the GRV lakes. In Kenya, few studies have quantified soil loss at national scales and erosion rates information on these GRV lakes’ regional basins within the ASALs is lacking. This study used the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates between 1990 and 2015 in the Great Rift Valley region of Kenya which is approximately 84.5% ASAL. The mean erosion rates for both periods was estimated to be tolerable (6.26 t ha−1 yr−1 and 7.14 t ha−1 yr−1 in 1990 and 2015 respectively) resulting in total soil loss of 116 Mt yr−1 and 132 Mt yr−1 in 1990 and 2015 respectively. Approximately 83% and 81% of the erosive lands in KGRV fell under the low risk category (<10 t ha−1 yr−1) in 1990 and 2015 respectively while about 10% were classified under the top three conservation priority levels in 2015. Lake Nakuru basin had the highest erosion rate net change (4.19 t ha−1 yr−1) among the GRV lake basins with Lake Bogoria-Baringo recording annual soil loss rates >10 t ha−1 yr−1 in both years. The mountainous central parts of the KGRV with Andosol/Nitisols soils and high rainfall experienced a large change of land uses to croplands thus had highest soil loss net change (4.34 t ha−1 yr−1). In both years, forests recorded the lowest annual soil loss rates (<3.0 t ha−1 yr−1) while most of the ASAL districts presented erosion rates (<8 t ha−1 yr−1). Only 34% of all the protected areas were found to have erosion rates <10 t ha−1 yr−1 highlighting the need for effective anti-erosive measures.

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An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction

Information ◽

10.3390/info11020079 ◽

2020 ◽

Vol 11 (2) ◽

pp. 79 ◽

Cited By ~ 2

Author(s):

Xiaoyu Han ◽

Yue Zhang ◽

Wenkai Zhang ◽

Tinglei Huang

Keyword(s):

Language Processing ◽

Large Scale ◽

Named Entity Recognition ◽

Relation Extraction ◽

Entity Recognition ◽

Additional Information ◽

Named Entity ◽

Proposed Model ◽

The Relationship ◽

Crucial Part

Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to be helpful in relation extraction. Additional information such as entity type getting by NER (Named Entity Recognition) and description provided by knowledge base both have their limitations. Nevertheless, there exists another way to provide additional information which can overcome these limitations in Chinese relation extraction. As Chinese characters usually have explicit meanings and can carry more information than English letters. We suggest that characters that constitute the entities can provide additional information which is helpful for the relation extraction task, especially in large scale datasets. This assumption has never been verified before. The main obstacle is the lack of large-scale Chinese relation datasets. In this paper, first, we generate a large scale Chinese relation extraction dataset based on a Chinese encyclopedia. Second, we propose an attention-based model using the characters that compose the entities. The result on the generated dataset shows that these characters can provide useful information for the Chinese relation extraction task. By using this information, the attention mechanism we used can recognize the crucial part of the sentence that can express the relation. The proposed model outperforms other baseline models on our Chinese relation extraction dataset.

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Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

Hydrology and Earth System Sciences ◽

10.5194/hess-19-3845-2015 ◽

2015 ◽

Vol 19 (9) ◽

pp. 3845-3856 ◽

Cited By ~ 9

Author(s):

F. Todisco ◽

L. Brocca ◽

L. F. Termite ◽

W. Wagner

Keyword(s):

Soil Moisture ◽

Soil Loss ◽

Large Scale ◽

Central Italy ◽

Remote Sensing Data ◽

Loss Estimation ◽

Balance Model ◽

Estimation Accuracy ◽

Soil Losses

Abstract. The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008–2013. The results showed that including soil moisture observations in the event rainfall–runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ~ 0.35 and a root mean square error (RMSE) of ~ 2.8 Mg ha−1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

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Evaluating of the spatial heterogeneity of soil loss tolerance and its effects on erosion risk in the carbonate areas of southern China

Solid Earth ◽

10.5194/se-8-661-2017 ◽

2017 ◽

Vol 8 (3) ◽

pp. 661-669 ◽

Cited By ~ 9

Author(s):

Yue Li ◽

Xiao Yong Bai ◽

Shi Jie Wang ◽

Luo Yi Qin ◽

Yi Chao Tian ◽

...

Keyword(s):

Soil Erosion ◽

Spatial Heterogeneity ◽

Soil Loss ◽

Carbonate Rock ◽

Southern China ◽

Clastic Rock ◽

Erosion Risk ◽

Soil Loss Tolerance ◽

Loss Tolerance ◽

The Relationship

Abstract. Soil loss tolerance (T value) is one of the criteria in determining the necessity of erosion control measures and ecological restoration strategy. However, the validity of this criterion in subtropical karst regions is strongly disputed. In this study, T value is calculated based on soil formation rate by using a digital distribution map of carbonate rock assemblage types. Results indicated a spatial heterogeneity and diversity in soil loss tolerance. Instead of only one criterion, a minimum of three criteria should be considered when investigating the carbonate areas of southern China because the one region, one T value concept may not be applicable to this region. T value is proportionate to the amount of argillaceous material, which determines the surface soil thickness of the formations in homogenous carbonate rock areas. Homogenous carbonate rock, carbonate rock intercalated with clastic rock areas and carbonate/clastic rock alternation areas have T values of 20, 50 and 100 t/(km2 a), and they are extremely, severely and moderately sensitive to soil erosion. Karst rocky desertification (KRD) is defined as extreme soil erosion and reflects the risks of erosion. Thus, the relationship between T value and erosion risk is determined using KRD as a parameter. The existence of KRD land is unrelated to the T value, although this parameter indicates erosion sensitivity. Erosion risk is strongly dependent on the relationship between real soil loss (RL) and T value rather than on either erosion intensity or the T value itself. If RL > > T, then the erosion risk is high despite of a low RL. Conversely, if T > > RL, then the soil is safe although RL is high. Overall, these findings may clarify the heterogeneity of T value and its effect on erosion risk in a karst environment.

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