scholarly journals A systematic assessment of uncertainties in large scale soil loss estimation from different representations of USLE input factors – A case study for Kenya and Uganda

2019 ◽  
Author(s):  
Christoph Schürz ◽  
Bano Mehdi ◽  
Jens Kiesel ◽  
Karsten Schulz ◽  
Mathew Herrnegger
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Patterns ◽  
Soil Loss ◽  
Field Data ◽  
Large Scale ◽  
Small Scale ◽  
Model Input ◽  
Study Results ◽  
Soil Losses

Abstract. The Universal Soil Loss Equation (USLE) is the most commonly used model to assess soil erosion by water. The model equation quantifies long-term average annual soil loss as a product of the rainfall erosivity R, soil erodibility K, slope length and steepness LS, soil cover C and support measures P. A large variety of methods exist to derive these model inputs from readily available data. However, the estimated values of a respective model input can strongly differ when employing different methods and can eventually introduce large uncertainties in the estimated soil loss. The potential to evaluate soil loss estimates at a large scale are very limited, due to scarce in-field observations and their comparability to long-term soil estimates. In this work we addressed (i) the uncertainties in the soil loss estimates that can potentially be introduced by different representations of the USLE input factors and (ii) challanges that can arise in the evaluation of uncertain soil loss estimates with observed data. In a systematic analysis we developed different representations of USLE inputs for the study domain of Kenya and Uganda. All combinations of the generated USLE inputs resulted in 756 USLE model setups. We assessed the resulting distributions in soil loss, both spatially distributed and on district level for Kenya and Uganda. In a sensitivity analysis we analyzed the contributions of the USLE model inputs to the ranges in soil loss and analyzed their spatial patterns. We compared the calculated USLE ensemble soil estimates to available in-field data and other study results and addressed possibilities and limitations of the USLE model evaluation. The USLE model ensemble resulted in wide ranges of estimated soil loss, exceeding the mean soil loss by over an order of magnitude particularly in hilly topographies. The study implies that a soil loss assessment with the USLE is highly uncertain and strongly depends on the realizations of the model input factors. The employed sensitivity analysis enabled us to identify spatial patterns in the importance of the USLE input factors. The C and K factors showed large scale patterns of importance in the densely vegetated part of Uganda and the dry north of Kenya, respectively, while LS was relevant in small scale heterogeneous patterns. Major challenges for the evaluation of the estimated soil losses with in-field data were due to spatial and temporal limitations of the observation data, but also due to measured soil losses describing processes that are different to the ones that are represented by the USLE.

scholarly journals A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda

2020 ◽  
Vol 24 (9) ◽  
pp. 4463-4489
Author(s):  
Christoph Schürz ◽  
Bano Mehdi ◽  
Jens Kiesel ◽  
Karsten Schulz ◽  
Mathew Herrnegger
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Patterns ◽  
Soil Loss ◽  
Field Data ◽  
Large Scale ◽  
Small Scale ◽  
Model Input ◽  
Study Results ◽  
Soil Losses

Abstract. The Universal Soil Loss Equation (USLE) is the most commonly used model to assess soil erosion by water. The model equation quantifies long-term average annual soil loss as a product of the rainfall erosivity R, soil erodibility K, slope length and steepness LS, soil cover C, and support measures P. A large variety of methods exist to derive these model inputs from readily available data. However, the estimated values of a respective model input can strongly differ when employing different methods and can eventually introduce large uncertainties in the estimated soil loss. The potential to evaluate soil loss estimates at a large scale is very limited due to scarce in-field observations and their comparability to long-term soil estimates. In this work we addressed (i) the uncertainties in the soil loss estimates that can potentially be introduced by different representations of the USLE input factors and (ii) challenges that can arise in the evaluation of uncertain soil loss estimates with observed data. In a systematic analysis we developed different representations of USLE inputs for the study domain of Kenya and Uganda. All combinations of the generated USLE inputs resulted in 972 USLE model setups. We assessed the resulting distributions in soil loss, both spatially distributed and on the administrative level for Kenya and Uganda. In a sensitivity analysis we analyzed the contributions of the USLE model inputs to the ranges in soil loss and analyzed their spatial patterns. We compared the calculated USLE ensemble soil estimates to available in-field data and other study results and addressed possibilities and limitations of the USLE model evaluation. The USLE model ensemble resulted in wide ranges of estimated soil loss, exceeding the mean soil loss by over an order of magnitude, particularly in hilly topographies. The study implies that a soil loss assessment with the USLE is highly uncertain and strongly depends on the realizations of the model input factors. The employed sensitivity analysis enabled us to identify spatial patterns in the importance of the USLE input factors. The C and K factors showed large-scale patterns of importance in the densely vegetated part of Uganda and the dry north of Kenya, respectively, while LS was relevant in small-scale heterogeneous patterns. Major challenges for the evaluation of the estimated soil losses with in-field data were due to spatial and temporal limitations of the observation data but also due to measured soil losses describing processes that are different to the ones that are represented by the USLE.

A systematic assessment of uncertainties in large scale soil loss estimation from different representations of USLE input factors – A case study for Kenya and Uganda

2020 ◽  
Author(s):  
Christoph Schürz ◽  
Bano Mehdi ◽  
Jens Kiesel ◽  
Karsten Schulz ◽  
Mathew Herrnegger
Keyword(s):  
Sensitivity Analysis ◽  
Soil Loss ◽  
Field Data ◽  
Large Scale ◽  
Loss Estimation ◽  
Observation Data ◽  
Systematic Assessment ◽  
Soil Losses

<p>The Universal Soil Loss Equation (USLE) is a standard model to assess soil erosion by water. The model equation quantifies long-term average annual soil loss as a product of the rainfall erosivity R, soil erodibility K, slope length and slope steepness LS, the soil cover C and support measures P. Several methods exist to derive each of the model inputs from readily available data. The estimated values of a model input, however, can strongly differ depending on the method that was applied. The multiplication of the input factors with the USLE eventually results in large uncertainties for the soil loss estimates. A comparison of the estimated soil loss to observation data can potentially reduce the uncertainties. Yet, for large scale soil loss estimations, in-field observations are rare and their comparability to long-term soil estimates is limited. This work puts a focus on uncertainty and sensitivity analysis in large scale soil loss estimation employing the USLE with different realizations of the USLE input factors.</p><p>In a systematic analysis we developed different representations of the USLE inputs for the study domain of Kenya and Uganda with a spatial resolution of 90 m. All combinations of the generated USLE inputs resulted in 756 USLE model setups. We assessed the resulting distributions in soil loss, both spatially distributed and on district level for Kenya and Uganda. In a sensitivity analysis we analyzed the contributions of the USLE model inputs to the ranges in soil loss and analyzed their spatial patterns. We compared the calculated USLE ensemble soil estimates to available in-field data and other study results and addressed possibilities and limitations of the USLE model evaluation.</p><p>The USLE model ensemble resulted in wide ranges of estimated soil loss, exceeding the mean soil loss by over an order of magnitude particularly in hilly topographies. The study implies that a soil loss assessment with the USLE is highly uncertain and strongly depends on the realizations of the model input factors. The employed sensitivity analysis enabled us to identify spatial patterns in the importance of the USLE input factors. The C and K factors showed large scale patterns of importance in the densely vegetated part of Uganda and the dry north of Kenya, respectively. The LS factor estimates were mostly relevant in small scale heterogeneous patterns. Major challenges for the evaluation of the estimated soil losses with in-field data were due to spatial and temporal limitations of the observation data, but also due to measured soil losses describing processes that are different to the ones that are represented by the USLE.</p><p>Reference: Schürz, C., Mehdi, B., Kiesel, J., Schulz, K., and Herrnegger, M.: A systematic assessment of uncertainties in large scale soil loss estimation from different representations of USLE input factors – A case study for Kenya and Uganda, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-602, in review, 2019. </p>

scholarly journals Suggestion of Measurement Management Criteria for Soil Slope Failure Based on Displacement

2021 ◽  
Vol 21 (1) ◽  
pp. 251-260
Author(s):  
Sun-Gyu Choi ◽  
Jae-Wook Suk ◽  
Hyang-Seon Jeong
Keyword(s):  
Literature Review ◽  
Field Data ◽  
Large Scale ◽  
Slope Failure ◽  
Small Scale ◽  
Soil Slope ◽  
Short Term ◽  
Literature Reviews ◽  
Under Construction

This paper describes the Measurement Management Criteria (MMC) of a soil slope failure based on displacement using literature reviews, small-scale experiments, large-scale experiments, and field data. Two types of measurement management criteria were developed, i.e., short-term criteria for slopes under construction or requiring urgent measurements, and long-term criteria for slopes under continuous management. First, the measurement criteria for the short term were determined based on small- and-large scale experiments, and were determined to be “1 mm/min for the watch level,” “4 mm/min for the caution level,” and “21 mm/min for the alert level.” Next, the criteria for the long term were determined through a literature review and field data, and were “2 mm/day for the watch level,” “8 mm/day for the caution level,” and “56 mm/day for the alert level”.

scholarly journals Soil Erosion Estimates in Arid Region: A Case Study of the Koutine Catchment, Southeastern Tunisia

2021 ◽  
Vol 11 (15) ◽  
pp. 6763
Author(s):  
Mongi Ben Zaied ◽  
Seifeddine Jomaa ◽  
Mohamed Ouessar
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Scientific Evidence ◽  
Field Measurements ◽  
Spatiotemporal Variability ◽  
Soil Conditions ◽  
Storm Events ◽  
Erosion Processes ◽  
Study Results ◽  
Soil Losses

Soil erosion remains one of the principal environmental problems in arid regions. This study aims to assess and quantify the variability of soil erosion in the Koutine catchment using the RUSLE (Revised Universal Soil Loss Equation) model. The Koutine catchment is located in an arid area in southeastern Tunisia and is characterized by an annual mean precipitation of less than 200 mm. The model was used to examine the influence of topography, extreme rainstorm intensity and soil texture on soil loss. The data used for model validation were obtained from field measurements by monitoring deposited sediment in settlement basins of 25 cisterns (a traditional water harvesting and storage technique) over 4 years, from 2015 to 2018. Results showed that slope is the most controlling factor of soil loss. The average annual soil loss in monitoring sites varies between 0.01 and 12.5 t/ha/y. The storm events inducing the largest soil losses occurred in the upstream part of the Koutine catchment with a maximum value of 7.3 t/ha per event. Soil erosion is highly affected by initial and preceding soil conditions. The RUSLE model reasonably reproduced (R2 = 0.81) the spatiotemporal variability of measured soil losses in the study catchment during the observation period. This study revealed the importance of using the cisterns in the data-scarce dry areas as a substitute for the classic soil erosion monitoring fields. Besides, combining modeling of outputs and field measurements could improve our physical understanding of soil erosion processes and their controlling factors in an arid catchment. The study results are beneficial for decision-makers to evaluate the existing soil conservation and water management plans, which can be further adjusted using appropriate soil erosion mitigation options based on scientific evidence.

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

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

2015 ◽  
Vol 19 (9) ◽  
pp. 3845-3856 ◽  
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.

Species Responses to Grazing and Environmental-Factors in an Arid Halophytic Shrubland Community

1987 ◽  
Vol 35 (2) ◽  
pp. 135 ◽  
Author(s):  
RB Hacker
Keyword(s):  
Environmental Factors ◽  
Soil Salinity ◽  
Large Scale ◽  
Chemical Properties ◽  
Grazing Pressure ◽  
Aerial Photographs ◽  
Small Scale ◽  
Factors Affecting ◽  
Species Responses

Species responses to grazing and environmental factors were studied in an arid halophytic shrubland community in Western Australia. The grazing responses of major shrub species were defined by using reciprocal averaging ordination of botanical data, interpreted in conjunction with a similar ordination of soil chemical properties and measures of soil erosion derived from large-scale aerial photographs. An apparent small-scale interaction between grazing and soil salinity was also defined. Long-term grazing pressure is apparently reduced on localised areas of high salinity. Environmental factors affecting species distribution are complex and appear to include soil salinity, soil cationic balance, geomorphological variation and the influence of cryptogamic crusts on seedling establishment.

scholarly journals Topophilia, Placemaking, and Boundary Work

2022 ◽  
Vol 6 (GROUP) ◽  
pp. 1-33
Author(s):  
Janghee Cho ◽  
Samuel Beck ◽  
Stephen Voida
Keyword(s):  
Large Scale ◽  
Methodological Approach ◽  
Work Practice ◽  
Small Scale ◽  
Work From Home ◽  
Humanistic Geography ◽  
Working From Home ◽  
Long Term Impact

The COVID-19 pandemic fundamentally changed the nature of work by shifting most in-person work to a predominantly remote modality as a way to limit the spread of the coronavirus. In the process, the shift to working-from-home rapidly forced the large-scale adoption of groupware technologies. Although prior empirical research examined the experience of working-from-home within small-scale groups and for targeted kinds of work, the pandemic provides HCI and CSCW researchers with an unprecedented opportunity to understand the psycho-social impacts of a universally mandated work-from-home experience rather than an autonomously chosen one. Drawing on boundary theory and a methodological approach grounded in humanistic geography, we conducted a qualitative analysis of Reddit data drawn from two work-from-home-related subreddits between March 2020 and January 2021. In this paper, we present a characterization of the challenges and solutions discussed within these online communities for adapting work to a hybrid or fully remote modality, managing reconfigured work-life boundaries, and reconstructing the home's sense of place to serve multiple, sometimes conflicting roles. We discuss how these findings suggest an emergent interplay among adapted work practice, reimagined physical (and virtual) spaces, and the establishment and continual re-negotiation of boundaries as a means for anticipating the long-term impact of COVID on future conceptualizations of productivity and work.

scholarly journals Low-cost Large Scale Vermicompost Unit

2021 ◽  
Vol 2115 (1) ◽  
pp. 012026
Author(s):  
Sonam Solanki ◽  
Gunendra Mahore
Keyword(s):  
Large Scale ◽  
Low Cost ◽  
Small Scale ◽  
Scale Production ◽  
Key Innovation ◽  
Main Challenge ◽  
Long Run ◽  
Large Scale Production ◽  
Term Maintenance

Abstract In the current process of producing vermicompost on a large-scale, the main challenge is to keep the worms alive. This is achieved by maintaining temperature and moisture in their living medium. It is a difficult task to maintain these parameters throughout the process. Currently, this is achieved by building infrastructure but this method requires a large initial investment and long-run maintenance. Also, these methods are limited to small-scale production. For large-scale production, a unit is developed which utilises natural airflow with water and automation. The main aim of this unit is to provide favourable conditions to worms in large-scale production with very low investment and minimum maintenance in long term. The key innovation of this research is that the technology used in the unit should be practical and easy to adopt by small farmers. For long-term maintenance of the technology lesser number of parts are used.

scholarly journals The role of plume-scale processes in long-term impacts of aircraft emissions

2019 ◽  
Author(s):  
Thibaud M. Fritz ◽  
Sebastian D. Eastham ◽  
Raymond L. Speth ◽  
Steven R. H. Barrett
Keyword(s):  
Atmospheric Chemistry ◽  
Large Scale ◽  
Maximum Error ◽  
Chemical Mechanism ◽  
Ozone Production ◽  
Lagrangian Model ◽  
Small Scale ◽  
Plume Model ◽  
Soot Emissions

Abstract. Emissions from aircraft engines contribute to atmospheric NOx, driving changes in both the climate and in surface air quality. Existing atmospheric models typically assume instant dilution of emissions into large-scale grid cells, neglecting non-linear, small-scale processes occurring in aircraft wakes. They also do not explicitly simulate the formation of ice crystals, which could drive local chemical processing. This assumption may lead to errors in estimates of aircraft-attributable ozone production, and in turn to biased estimates of aviation’s current impacts on the atmosphere and the effect of future changes in emissions. This includes soot emissions, on which contrail ice forms. These emissions are expected to reduce as biofuel usage increases, but their chemical effects are not well captured by existing models. To address this problem, we develop a Lagrangian model which explicitly models the chemical and microphysical evolution of an aircraft plume. It includes a unified tropospheric-stratospheric chemical mechanism that incorporates heterogeneous chemistry on background and aircraft-induced aerosols. Microphysical processes are also simulated, including the formation, persistence, and chemical influence of contrails. The plume model is used to quantify how the long-term (24-hour) atmospheric chemical response to an aircraft plume varies in response to different environmental conditions, and engine characteristics, and fuel properties. We find that an instant dilution model consistently overestimates ozone production compared to the plume model, up to a maximum error of ~ 200 % at cruise altitudes. Instant dilution of emissions also underestimates the fraction of remaining NOx, although the magnitude and sign of the error vary with season, altitude, and latitude. We also quantify how changes in soot emissions affect plume behavior. Our results show that a 50 % reduction in black carbon emissions, as may be possible through blending with certain biofuels, leads to contrails which evaporate ~ 9 % faster and are 14 % optically thinner. The conversion of emitted NOx to HNO3 and N2O5 falls by 65 % and 69 % respectively, resulting in chemical feedbacks which are not resolved by instant-dilution approaches. The persistent discrepancies between results from the instant dilution approach and from the aircraft plume model demonstrate that a parametrization of effective emission indices should be incorporated into 3-D atmospheric chemistry transport models.

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