Application of a physically based soil erosion model, GUEST, in the absence of data on runoff rates II.

Soil Research ◽  
1999 ◽  
Vol 37 (1) ◽  
pp. 13 ◽  
Author(s):  
B. Yu ◽  
C. W. Rose ◽  
A. Sajjapongse ◽  
D. Yin ◽  
Z. Eusof ◽  
...  
Keyword(s):  
Soil Loss ◽  
Storm Event ◽  
Soil Erodibility ◽  
Storm Events ◽  
Erosion Model ◽  
Physically Based ◽  
Runoff Rate ◽  
Model Efficiency ◽  
Coarse To Fine ◽  
Event Basis

Runoff rates were estimated from rainfall rates and runoff amounts for 4 experimental sites in China, Malaysia, and Thailand before a physically based erosion model GUEST was used to determine the soil erodibility parameter and evaluate the potential to use the erosion model to predict the amount of soil loss on an event basis. We also examined 3 different ways of determining the soil erodibility parameter for the same storm event using: (i) hydrographs estimated from rainfall intensities and runoff amounts; (ii) an effective runoff rate calculated from the hydrograph; (iii) an estimate of the effective runoff rate based on a scaling technique involving the peak rainfall intensity and the gross runoff coefficient. All 3 methods can produce consistent soil erodibility parameters for a given runoff event. The calculated soil erodibility for individual storm events for all sites shows considerable temporal variation and for most sites a decreasing trend over time, as observed elsewhere in the same region. Among the 4 soils examined, the average soil erodibility tends to decrease as the ratio of coarse to fine materials decreases. When the erosion model GUEST is used to predict event soil loss using estimated soil erodibility parameters, an average model efficiency of 0·68 is achieved for the sites tested.

Application of a physically based soil erosion model, GUEST, in the absence of data on runoff rates I. Theory and methodology

Soil Research ◽  
1999 ◽  
Vol 37 (1) ◽  
pp. 1 ◽  
Author(s):  
B. Yu ◽  
C. W. Rose
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Soil Erodibility ◽  
Erosion Model ◽  
Erosion Models ◽  
Loss Data ◽  
Physically Based ◽  
Set Up ◽  
Data Requirements

When physically based erosion models such as GUEST are used to determine soil erodibility parameters or to predict the rate of soil loss, data on runoff rates, as distinct from event runoff amount, are often needed. Data on runoff rates, however, are not widely available. This paper describes methods that can be used to overcome this lack of data on runoff rates. These methods require only rainfall rates and runoff amounts, which are usually available for sites set up primarily to test and validate the USLE technology. In addition, the paper summarises the data requirements for the erosion model GUEST and application procedures. In the accompanying paper, these methods are applied to 4 experimental sites in the ASIALAND Network.

Toward a framework for runoff and soil loss prediction using GUEST technology

Soil Research ◽  
1997 ◽  
Vol 35 (5) ◽  
pp. 1191 ◽  
Author(s):  
B. Yu ◽  
C. W. Rose ◽  
C. A. A. Ciesiolka ◽  
K. J. Coughlan ◽  
B. Fentie
Keyword(s):  
Soil Loss ◽  
Field Data ◽  
Rainfall Intensity ◽  
Rainfall Amount ◽  
Balance Model ◽  
Erosion Processes ◽  
Total Runoff ◽  
Physically Based ◽  
Loss Prediction ◽  
Runoff Rate

In recent years, a number of physically based models have been developed for soil loss predictions. GUEST is one such model based on fundamental physical principles and the current understanding of water erosion processes. GUEST is mainly used to determine a soil erodibility parameter. To apply the model in a predictive mode, the model is simplified in a physically meaningful manner for flow-driven erosion processes, and 2 essential hydrologic variables are identified, namely total runoff amount and an effective runoff rate. These variables are required to determine soil loss for individual runoff events. A simple water balance model was developed and used to predict runoff amount from rainfall amount. The efficiency of this runoff amount model in prediction was over 90% using field data. A 1-parameter regression model (r2 ~ 0·9) for the effective runoff rate was also established which uses peak rainfall intensity in addition to rainfall and runoff amounts. The prediction of peak rainfall intensity for a given rainfall amount and storm type was also sought. The field data were from Goomboorian, near Gympie, in south-east Queensland and these data were used to test and validate both models. Results overall are satisfactory and the approach adopted is promising. A framework for soil loss prediction is established within which individual parts can be further refined and improved.

scholarly journals Storm-wise sediment yield prediction using hillslope erosion model in semi-arid abundant lands

2013 ◽  
Vol 8 (No. 1) ◽  
pp. 42-48 ◽  
Author(s):  
S. Fazli ◽  
H. Noor
Keyword(s):  
Soil Erosion ◽  
Sediment Yield ◽  
Land Surface ◽  
Soil Conservation ◽  
Soil Erodibility ◽  
Storm Events ◽  
Erosion Model ◽  
Sediment Yields ◽  
Slope Steepness ◽  
Erosion Models

Evaluation of soil erosion by existing models is needed as an important tool for managerial purposes in designation of proper water and soil conservation techniques. The present study aimed to assess the applicability of hillslope erosion model (HEM) as one of the newest erosion models for prediction of storm-wise sediment yield in Khosbijan rangeland with 20% slope steepness by using soil erosion standard plots. In order to run the model, runoff depth, land surface cover, soil texture, slope steepness and length were determined for 16 storm events. The results showed that the uncalibrated HEM did not simulate the observed sediment yields properly. Calibration of soil erodibility parameter and developing regression between observed and estimated data indicated that the model was capable of predicting sediment yield in plots by applying soil erodibility parameter of 0.15 with determination coefficient of 0.64 and estimate error of 40%. 

Evaluation of the CREAMS model. II. Use of rainulator data to derive soil erodibility parameters and prediction of field soil losses using derived parameters

Soil Research ◽  
1989 ◽  
Vol 27 (3) ◽  
pp. 563 ◽  
Author(s):  
RJ Loch ◽  
DM Silburn ◽  
DM Freebairn
Keyword(s):  
Soil Loss ◽  
Confidence Regions ◽  
Soil Erodibility ◽  
Field Soil ◽  
Erosion Process ◽  
Erosion Model ◽  
Erosion Processes ◽  
Wide Range ◽  
Best Fit ◽  
Soil Losses

The first paper of this series identified several parameters to which predictions of the CREAMS erosion model were sensitive. Two of these, soil erodibility parameters nbov and K, cannot be measured directly. Therefore, this paper reports procedures for deriving nbov and K for the CREAMS erosion model from rainulator data. The procedures identify confidence regions for best fit combinations of nbov and K. Fairly specific values of nbov were obtained, but a wide range of K values provided similar near-optimal predictions. Optimum regions of nbov and K for plots where rilling was the dominant erosion process were significantly different to those for plots dominated by rain-flow erosion for two of the three soils studied. The parameters derived from rainulator data were used to predict soil losses for field catchments on two clay soils, for conditions where surface cover by stubble was 110%. Measured rainfall and runoff were used as inputs to the model. For an Irving clay site, the model gave excellent prediction of field soil losses. For a Moola clay site, the larger soil losses were considerably underpredicted, because resistance to rilling on the rainulator plots resulted in lower K values than required for erosion events where rilling is fully developed. The need for parameters to be derived from data reflecting the erosion processes of importance to field soil loss is illustrated. Nonetheless, this study shows that parameters for the CREAMS model can be obtained satisfactorily from rainulator data, provided that the erosion processes studied are relevant, and that there is sufficient replication within the rainulator studies.

scholarly journals Comparing watershed soil erosion of Taiwan and Thailand

2018 ◽  
Vol 192 ◽  
pp. 02041
Author(s):  
Yi-Hsin Liu ◽  
Kieu Anh Nguyen ◽  
Walter Chen ◽  
Jatuwat Wattanasetpong ◽  
Uma Seeboonruang
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Soil Erodibility ◽  
Erosion Model ◽  
Shihmen Reservoir ◽  
Soil Erodibility Factor ◽  
Tropical Watersheds ◽  
Reservoir Watershed ◽  
Soil Loss Equation ◽  
Steepness Factor

Tropical watersheds in Taiwan and Thailand face the same severe soil erosion problem that is increasing at an alarming rate. In order to evaluate the severity of soil erosion, we quantitatively investigate the issue using a common soil erosion model (Universal Soil Loss Equation, USLE) on the Shihmen reservoir watershed of Taiwan and the Lam Phra Ploeng basin of Thailand, and compare their respective erosion factors. The results show an interesting contrast between the two watersheds. Some of the factors (rainfall factor, slope-steepness factor) are higher in the Shihmen reservoir watershed, while others (soil erodibility factor, cover and management factor) are higher in the Lam Phra Ploeng basin. The net result is that these factors cancel each other out, and the amount of soil erosion of the two watersheds are very similar at 68.03 t/ha/yr and 67.57 t/ha/yr, respectively.

scholarly journals Comparison of the Applicability of Different Soil Erosion Models to Predict Soil Erodibility Factor and Event Soil Losses on Loess Slopes in Hungary

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3517
Author(s):  
Boglárka Keller ◽  
Csaba Centeri ◽  
Judit Alexandra Szabó ◽  
Zoltán Szalai ◽  
Gergely Jakab
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Coefficient Of Determination ◽  
Soil Erodibility ◽  
The European Union ◽  
Extreme Precipitation Events ◽  
Erosion Models ◽  
Physically Based ◽  
Physically Based Models ◽  
Soil Losses

Climate change induces more extreme precipitation events, which increase the amount of soil loss. There are continuous requests from the decision-makers in the European Union to provide data on soil loss; the question is, which ones should we use? The paper presents the results of USLE (Universal Soil Loss Equation), RUSLE (Revised USLE), USLE-M (USLE-Modified) and EPIC (Erosion-Productivity Impact Calculator) modelling, based on rainfall simulations performed in the Koppány Valley, Hungary. Soil losses were measured during low-, moderate- and high-intensity rainfalls on cultivated soils formed on loess. The soil erodibility values were calculated by the equations of the applied soil erosion models and ranged from 0.0028 to 0.0087 t ha h ha−1 MJ−1 mm−1 for the USLE-related models. EPIC produced larger values. The coefficient of determination resulted in an acceptable correlation between the measured and calculated values only in the case of USLE-M. Based on other statistical indicators (e.g., NSEI, RMSE, PBIAS and relative error), RUSLE, USLE and USLE-M resulted in the best performance. Overall, regardless of being non-physically based models, USLE-type models seem to produce accurate soil erodibility values, thus modelling outputs.

Simulation of nonpoint source pollutant loadings from urban area during rainfall: an application of a physically-based distributed model

1998 ◽  
Vol 38 (10) ◽  
pp. 199-206 ◽  
Author(s):  
Zhang Haiping ◽  
Kiyoshi Yamada
Keyword(s):  
Urban Area ◽  
Overland Flow ◽  
Drainage System ◽  
Distributed Model ◽  
Storm Event ◽  
Urban Watershed ◽  
Storm Events ◽  
Runoff Generation ◽  
Time Step ◽  
Physically Based

A physically-based, distributed model, PROUW, is applied to a small urban watershed in Japan with an area of 66.18 ha. The model includes a description of evapotranspiration, percolation, runoff generation, overland flow routing, pollutant accumulation in dry weathers and washoff during storm events, overland pollutant routing, and flow and pollutant routing in drainage system. The finite difference schematization of the urban watershed provides a representation of the spatial pattern of topography, land-use, soil types and meteorological inputs. The watershed is divided into 7500 grids of 10m × 10m and the runoff rate and pollutant loadings are simulated with a time step of 5 sec. The data for the storm event of April 28, 1995 is used for model calibration. Simulated hydrograph and pollutographs of the storm event of April 18, 1995 are compared with the observed data. Results show a reasonable degree of fit, indicating that the model provides a reasonable interpretation of the overall runoff and pollutant generation processes in the urban area. The results also suggest that the model should be improved further by incorporating new reliable equations for pollutant washoff estimation.

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 Identifying erosive periods by using RUSLE factors in mountain fields of the Central Spanish Pyrenees

2008 ◽  
Vol 12 (2) ◽  
pp. 523-535 ◽  
Author(s):  
M. López-Vicente ◽  
A. Navas ◽  
J. Machín
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Loss ◽  
Rainfall Intensity ◽  
Soil Moisture Content ◽  
Rainfall Erosivity ◽  
Soil Erodibility ◽  
Maximum Rainfall ◽  
Freeze Thaw ◽  
Soil Losses

Abstract. The Mediterranean environment is characterized by strong temporal variations in rainfall volume and intensity, soil moisture and vegetation cover along the year. These factors play a key role on soil erosion. The aim of this work is to identify different erosive periods in function of the temporal changes in rainfall and runoff characteristics (erosivity, maximum intensity and number of erosive events), soil properties (soil erodibility in relation to freeze-thaw processes and soil moisture content) and current tillage practices in a set of agricultural fields in a mountainous area of the Central Pyrenees in NE Spain. To this purpose the rainfall and runoff erosivity (R), the soil erodibility (K) and the cover-management (C) factors of the empirical RUSLE soil loss model were used. The R, K and C factors were calculated at monthly scale. The first erosive period extends from July to October and presents the highest values of erosivity (87.8 MJ mm ha−1 h−1), maximum rainfall intensity (22.3 mm h−1) and monthly soil erosion (0.25 Mg ha−1 month−1) with the minimum values of duration of erosive storms, freeze-thaw cycles, soil moisture content and soil erodibility (0.007 Mg h MJ−1 mm−1). This period includes the harvesting and the plowing tillage practices. The second erosive period has a duration of two months, from May to June, and presents the lowest total and monthly soil losses (0.10 Mg ha−1 month−1) that correspond to the maximum protection of the soil by the crop-cover ($C$ factor = 0.05) due to the maximum stage of the growing season and intermediate values of rainfall and runoff erosivity, maximum rainfall intensity and soil erodibility. The third erosive period extends from November to April and has the minimum values of rainfall erosivity (17.5 MJ mm ha−1 h−1) and maximum rainfall intensity (6.0 mm h−1) with the highest number of freeze-thaw cycles, soil moisture content and soil erodibility (0.021 Mg h MJ−1 mm−1) that explain the high value of monthly soil loss (0.24 Mg ha−1 month−1). The interactions between the rainfall erosivity, soil erodibility, and cover-management factors explain the similar predicted soil losses for the first and the third erosive periods in spite of the strong temporal differences in the values of the three RUSLE factors. The estimated value of annual soil loss with the RUSLE model (3.34 Mg ha−1 yr−1) was lower than the measured value with 137Cs (5.38 Mg ha−1 yr−1) due to the low values of precipitation recorded during the studied period. To optimize agricultural practices and to promote sustainable strategies for the preservation of fragile Mediterranean agrosystems it is necessary to delay plowing till October, especially in dryland agriculture regions. Thus, the protective role of the crop residues will extend until September when the greatest rainfall occurs together with the highest runoff erosivity and soil losses.

scholarly journals The effects of the big storm events in the first half of 2015 on the radiation belts observed by EPT/PROBA-V

2016 ◽  
Vol 34 (1) ◽  
pp. 75-84 ◽  
Author(s):  
V. Pierrard ◽  
G. Lopez Rosson
Keyword(s):  
Charged Particle ◽  
High Resolution ◽  
Geomagnetic Storm ◽  
Energetic Particle ◽  
High Energy ◽  
Radiation Belts ◽  
Radiation Environment ◽  
Storm Event ◽  
Storm Events ◽  
Particle Radiation

Abstract. With the energetic particle telescope (EPT) performing with direct electron and proton discrimination on board the ESA satellite PROBA-V, we analyze the high-resolution measurements of the charged particle radiation environment at an altitude of 820 km for the year 2015. On 17 March 2015, a big geomagnetic storm event injected unusual fluxes up to low radial distances in the radiation belts. EPT electron measurements show a deep dropout at L > 4 starting during the main phase of the storm, associated to the penetration of high energy fluxes at L < 2 completely filling the slot region. After 10 days, the formation of a new slot around L = 2.8 for electrons of 500–600 keV separates the outer belt from the belt extending at other longitudes than the South Atlantic Anomaly. Two other major events appeared in January and June 2015, again with injections of electrons in the inner belt, contrary to what was observed in 2013 and 2014. These observations open many perspectives to better understand the source and loss mechanisms, and particularly concerning the formation of three belts.

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