Abstract
The sediment yield model of the MUSLE (modified universal soil loss equation) is applied extensively throughout the world, but different performances have been reported of its success relative to measured data. A review of all the available literature is presented to assess the application of the model under different conditions and, ultimately, make a comprehensive judgement on the different aspects to allow readers to adjust their further research. A review of 49 papers showed the variable accuracy of the model, which depends on the manner of calculation and determination of the input and output, and the study time and space scales. There were differences in land use, in correspondence of the physiographic characteristics with those of the original conditions of model development, and even in the experience of researchers in applying the model. The results also show the need to consider the original application of the model, as proposed by its developers, to achieve comparable results.
Editor Z.W. Kundzewicz
Citation Sadeghi, S.H.R., et al., 2014. A review of the application of the MUSLE model worldwide. Hydrological Sciences Journal, 59 (2), 365–375.
Résumé
Le modèle de production de sédiments de l’équation universelle modifiée des pertes de terre (Modified Universal Soil Loss Equation—MUSLE) est largement appliqué dans le monde entier, mais des performances variées ont été signalées quant à son applicabilité pour les objectifs proposés. Nous présentons une revue de toute la littérature disponible pour évaluer l’application du modèle dans des conditions différentes et, à terme, pour porter un jugement complet sur les différents aspects de cette application, de manière à permettre aux lecteurs d’ajuster leurs recherches futures. L’examen de plus de 49 articles a confirmé la précision extrêmement variable du modèle en fonction du mode de calcul et de détermination des entrées et sorties, et des échelles temporelles et spatiales d’étude. Des différences existaient dans l’occupation des sols, la correspondance entre les caractéristiques physiographique d’étude et celles utilisées lors du développement du modèle, et même dans l’expérience des chercheurs dans l’application du modèle. Les résultats montrent aussi la nécessité de prendre en considération les conditions originales d’application du modèle, tel que cela est suggéré par ses développeurs, afin d’obtenir des résultats comparables.
INTRODUCTION
Accelerated soil erosion has detrimental effects on productivity, income distribution and the environment at national and global scales. Erosion phenomena and sediment transport in channels and rivers are the most complex issues in a watershed. The heavy erosion and continuous transmission of sediment is not only the cause of an imbalance of natural rivers and streams, but also the cause of change in the river channel and sediment accumulation behind dams reducing their storage volumes.
The rate of soil erosion has dramatically increased during recent decades and globally has been reported as 0.5, 0.75, 1 and 2.2 × 109 t in 1951, 1961, 1971 and 1993, respectively (Hosseini and Ghorbani Citation2005). However, not only are these figures unreliable, but they need to be updated frequently. Consequently, regular estimation of soil erosion or its consequences, such as sediment yield, is a must, which basically can be realized by applying appropriate models. Soil erosion process models have generally been developed in particular places in the world and exported to other parts, and some have been extensively applied. Therefore, assessment of their applicability and soundness is important for proper calibration of models, or for drawing necessary conclusions and designating true strategies.
Among soil erosion models, the universal soil loss equation (USLE) (Wischmeier and Smith Citation1965, Citation1978) is the most widely used, and misused, soil loss estimation equation in the world (Kinnell Citation2001). The USLE was originally applied to the prediction of soil losses from agriculture in the USA, in order to preserve soil resources, but has been extended for use in numerous countries (Kinnell Citation2001). This model was obtained for soil loss estimation based on 10 000 plot-years of data using field experiments under natural or simulated rainfalls in the USA (Kinnell Citation2001). The USLE, with some modifications and revisions, is still a useful tool in watershed management. A large number of existing erosion and sediment transport models are based on the USLE (Sadeghi et al. Citation2007a). Their application is, however, limited to the environmental circumstances from which the USLE was generated (Aksoy and Kavvas Citation2005). Since the USLE was developed for estimation of the annual soil loss from small plots of an area of some 40 m2, its application to individual storm events and large areas leads to large errors (Hann et al. Citation1994, Sadeghi Citation2004, Sadeghi and Mahdavi Citation2004, Kinnell Citation2005, Chang Citation2006, Sadeghi et al. Citation2007a), but its accuracy increases if it is coupled with a hydrologic rainfall-excess model (Novotny and Olem Citation1994, Sadeghi and Mahdavi Citation2004). One problem with the USLE model is that there is no direct consideration of runoff, although erosion depends on sediment being discharged with flow, which varies with runoff and sediment concentration (Kinnell Citation2005). Yet, Banasik (Citation1985) showed that application of the USLE with a sediment delivery ratio (SDR) is possible for computing sediment yield from small watersheds in Poland.
However, using the SDR in conjunction with watershed gross erosion, estimated by the soil erosion model as an estimation method, is tedious and inadequate if one is interested in single storms. Unless one is already available, developing an SDR model may involve parameters similar to those of the USLE and other models that are used to estimate gross erosion, a duplicate step and time-consuming process. Stream sediment is affected by the carrying capacity and deposition processes of overland flow. However, the storm event factor used by the USLE often fails to account for the effective rainfall that generates surface runoff. Also, the SDR varies with storms; the assumption of a constant SDR adds another source of error to the estimates (Williams Citation1977, Chang Citation2006, Sadeghi et al. Citation2007a, Citation2008). An improved erosivity factor was therefore introduced by Williams (Citation1975, Citation1977) and Foster et al. (Citation1977) to also take into account the runoff shear stress effect in terms of the product of runoff volume and peak discharge, on soil detachment for single storms. The approach of Williams and Berndt (Citation1977) in developing a modified version of the USLE was to derive a sediment yield estimation model based on runoff characteristics as the best single indicator for storm-event sediment yield prediction at the watershed outlet (Williams Citation1975, Beasley et al. Citation1980, Sadeghi and Mahdavi Citation2004, Hrissanthou Citation2005, Mishra et al. Citation2006, Sadeghi et al. Citation2007a, Citation2007b, Mishra and Ravibabu Citation2009) and some factors affecting soil erosion. Williams (Citation1975) showed that the estimate of stream sediment yield for individual storms could be simplified by using the USLE with its rainfall factor (R) replaced by a runoff factor. He developed the following revised form of the USLE using 778 storm-runoff events collected from 18 small watersheds, with areas varying from 15 to 1500 ha, slopes from 0.9 to 5.9% and slope lengths of 78.64 to 173.74 m (Williams and Berndt Citation1977, Hann et al. Citation1994) and called it the modified universal soil loss equation (MUSLE). The MUSLE was given in the following general form:
The MUSLE has been applied to many different watersheds around the world and for different purposes (Asokan Citation1981, Das Citation1982, Nicks et al. Citation1994, Banasik and Walling Citation1996, Kinnell and Riss Citation1998, Erskine et al. Citation2002, Khajehie et al. Citation2002, Rezaiifard et al. Citation2002, Kandrika and Dwivedi Citation2003, Cambazoglu and Gogos Citation2004, Fontes et al. Citation2004, Sadeghi Citation2004, Sarkhosh et al. Citation2004, Kandrika and Venkataratnam Citation2005, Varvani et al. Citation2006, Sadeghi et al. Citation2007a, Citation2007b, Citation2008, Khaledi Darvishan et al. Citation2009, Zhang et al. Citation2009, Lpez-Tarazn et al. Citation2012), and this model was modified in some cases. Because the MUSLE model was produced for specific conditions, its application without calibration has resulted in huge errors. Therefore, the present review was made to evaluate the application conditions and methods used to determine the MUSLE model variables in previous research.
MATERIALS AND METHODS
To review the application and the performance of the MUSLE model across the world, the available research records were first collected from related conference articles, journal papers and other scientific documents. Based on the available information in the documents, the details were evaluated as to the methodology used in determining the different input variables that appear in equation (1), namely runoff volume and peak, soil erodibility, topographic factors of slope steepness and length and crop management and control practice factors were extracted. The results of the model application, as well as its performance evaluation, were examined according to the available data or methodology explained in the documents, and also by reviewing the observed and estimated results. Finally, the possible alternatives for model calibration and any type of modification were evaluated to reduce the systematic or random errors.
RESULTS
The results of the review of use of the MUSLE model in many parts of the world, other details regarding the application and quality of the model calibration and the overall assessment of the research methodology described in the previous section, are summarized in .
Table 1 Details of application of the MUSLE in different parts of the world (WMd: Wischmeier and Smith diagram; N.A.: Not provided or unavailable data or information).
DISCUSSION
As seen in , the MUSLE model has been used in a variety of conditions and from different perspectives. The input variables have been determined or estimated through various approaches with different levels of accuracy. It is interesting to note from that, in some cases, no calibration or modification has been made in the MUSLE, despite the weak performances resulting from application of the MUSLE. Most of the studies were conducted in Asia, North America and Europe, with several studies also in Iran, especially during the last 10 years. The minimum, median and maximum values of the watershed areas to which the models have been applied are 0.04, 1713 and 386 000 ha, respectively. Few studies have been done in experimental plots (e.g. Golson et al. Citation2000, Sadeghi et al. Citation2008), or at the field scale (e.g. McConkey et al. Citation1997), so the proportions of studies at the watershed, plot and field scales are 90, 7 and 3%, respectively. The results of the review also showed that the model could not provide appropriate estimates in experimental plots, except at the Thomas research station (Golson et al. Citation2000). This can be attributed to the dissimilarity of conditions and governing processes between areas where the model was originally developed and the plots applied in different studies.
The results on erodibility factor showed that the values were obtained by using available information, with the help of the Wischmeier and Smith diagram in 60.87% of studies, and by using individual sampling (Cordova Citation1981, Smith et al. Citation1984, Jackson et al. Citation1987, Banasik et al. Citation1988, Erskine et al. Citation2002, Mahmoudzadeh et al. Citation2002, Cambazoglu and Gogos Citation2004, Appel et al. Citation2006, Ma Citation2006) and seasonal sampling (McConkey et al. Citation1997) in 13.04% and 2.17% of the studies, respectively. But the method of estimation of the erodibility factor was not given in 23.91% of studies. The results also showed that the erodibility estimation methods did not affect the accuracy of the model estimates.
The topography factor was estimated by the direct use of a topographic map at a scale of 1:50 000 in 43.48% of studies, with the help of a geographic information system (GIS) in 26.09% (Blaszczynski Citation2003, Chen and Mackay Citation2004, Basson Citation2005, Appel et al. Citation2006, Ma Citation2006, Mishra et al. Citation2006, Arekhi Citation2007, Jaramillo Citation2007, Pandey et al. Citation2009, Zhang et al. Citation2009) and by direct field measurement in 13.04% of the studies (); 14.39% of studies did not provide the methodology. The results showed that the use of GIS could improve performance of the model estimates.
The crop management and control practice factors were estimated by using existing data (34.78% of studies), the Laflen and Moldenhauer (Citation2003) classification (4.35%), GIS (26.09%) and field measurement (21.74%); 13.04% of studies did not note the method of estimation. The results also showed that considering the temporal variation of these factors could significantly improve the performance of the model, although it has been rarely taken into account. The crop management and control practice factors were estimated with the help of the available tables and generating a weighted average, and through field measurement ().
The peak flow and the volume of runoff were obtained through direct measurement of runoff on a storm-event basis (58.70% of studies), using existing data (6.52%) (Khajehie et al. Citation2002, Rezaiifard et al. Citation2002, Porabdullah Citation2005), applying GIS (6.52%) (Arekhi Citation2007), lumped runoff values (8.70%) (Jackson et al. Citation1987, McConkey et al. Citation1997, Erskine et al. Citation2002, Mahmoudzadeh et al. Citation2002), the Soil Conservation Service (SCS) method (10.87%) (Williams Citation1977, Blaszczynski Citation2003, Mishra et al. Citation2006, Chakrabarty et al. Citation2007, Zhang et al. Citation2009) and reverse routing (2.17%) (Sadeghi and Mahdavi Citation2004). In 6.52% of studies, no details were given. Our analysis also demonstrated the greater appropriateness of field and direct measurements of runoff on a storm-event basis for better performance of the model output compared to use of indirect methods. Recently, the MUSLE has frequently been used as a module for hydrological models, such as the Soul and Water Assessment Tool (SWAT) (Qiu et al. Citation2012, Yang et al. Citation2012), to estimate sediment yield. In these studies, the MUSLE is used in its original form and no modification is usually considered. In some cases, the weakness of the main model is attributed to its dependence on many empirical and semi-empirical models, such as SCS-curve number and MUSLE, which cause the main model to have less accuracy.
The 49 MUSLE applications evaluated showed that the MUSLE model has been applied for different purposes of sediment yield estimation, i.e. on a storm basis (in 73.91% of studies; see ), on a monthly basis (2.17%) (Shen et al. Citation2009) and an annual basis (17.39%; ), as well as for estimation of soil erosion on a storm-wise scale (Esmali and Abedini Citation2009), for pollutant estimation (Noor et al. Citation2010) and annual sediment yield with different return periods (in 2.17% of cases each). While the MUSLE model has been basically developed for estimation of sediment yield from large storm events occurring on rangeland watersheds (Williams and Berndt Citation1977), its application in other conditions was found by other researchers to generate high errors sometimes very different from the observed data. The research reports assessed here show application of the MUSLE model in various land-use scenarios (with percentage of studies): pasture (17.39%), agricultural (6.52%), forest (15.22%), pasture-agricultural (10.87%), forest-pasture (2.17%), forest-agricultural-urban (10.87%), forest-pasture-agricultural (4.35%) and agricultural-urban (2.17%); the type of land use was not reported in 15.22% of studies.
Owing to differences between observed and estimated values, attempts have been made to calibrate the MUSLE through adjusting the power or the coefficient of models in some studies (Jackson et al. Citation1987, Epifanio et al. Citation1991, McConkey et al. Citation1997, Khajehie et al. Citation2002, Rezaiifard et al. Citation2002, Sadeghi et al. Citation2004, Citation2007b, Sarkhosh et al. Citation2004, Khaledi Darvishan et al. Citation2009, Noor et al. Citation2010). In two studies (Chen and Mackay Citation2004, Varvani et al. Citation2006), only the power of the model was calibrated, which is logically more acceptable. The necessity of model calibration was also emphasized in those studies in which no calibration adjustment had been made. The minimum, median, maximum and standard deviation of the coefficient of the MUSLE in all the studies were found to be 0.001, 0.15, 6.38 and 17.25, respectively. Out of 46 studies, almost 22% had included calibration of the coefficient, whereas another 50% gave appropriate results. In the remaining 28%, the coefficient was not revised, although the necessity of calibration was emphasized. The minimum, median, maximum and standard deviation of the model power were calculated as 0.081, 0.745, 0.70, 1.12 and 0.3, respectively. The model power was calibrated in only 28.26% of the studies; another 43.48% did not undertake any calibration because they produced reasonable results, whereas, for the rest, revision is needed.
Our results also showed overestimation by the MUSLE model in some studies, while in other studies, the model underestimated the measured values (see ). In other cases, conducted in USA watersheds (Williams Citation1977, Jackson et al. Citation1987, Santos and Canino Citation1997, Golson et al. Citation2000, Zhang et al. Citation2009), or under similar climatic conditions to that of the original location (), the model presented good estimates.
According to the results of the present study, it can be concluded that the application of the MUSLE model may produce reasonable estimates when it is applied under appropriate conditions similar to those where the original model was developed () or calibrated accordingly. In this context, the MUSLE model values showed a significant difference with measured sediment yield in many watersheds in Iran (Afcheh, Amameh, Shahrchaii, Gharehchi, Chehelgazi and Kojor), the USA (Pheasant Branch and Foothill Range Field Station), western Canada, Kenya (Nyando and Nzoia) and Japan (Mie). The MUSLE model was then calibrated in these study areas (Jackson et al. Citation1987, Epifanio et al. Citation1991, McConkey et al. Citation1997, Khajehie et al. Citation2002, Rezaiifard et al. Citation2002, Chen and Mackay Citation2004, Sadeghi and Mahdavi Citation2004, Sadeghi et al. Citation2004, Citation2007b, Ma Citation2006, Varvani et al. Citation2006, Khaledi Darvishan et al. Citation2009, Noor et al. Citation2010). The model presented reliable results for sediment yield on a storm basis after calibration and with a low level of estimation error (Sadeghi et al. Citation2007b), as originally developed by Williams (Citation1975). Therefore, the unusual application of the MUSLE model, i.e. for estimation of soil erosion (Sadeghi et al. Citation2004, Esmali and Abedini Citation2009) or nutrient loss (Noor et al. Citation2010) provides inappropriate predictions at the watershed scale, or even at the plot scale (Sadeghi Citation2004, Kinnell Citation2005, Citation2010, Khaledi Darvishan Citation2009).
However, an accurate estimation of sediment yield requires a sufficient number of samples or sedimentgraph preparation to give an appropriate basis for comparison and model calibration (Cordova Citation1981, Smith et al. Citation1984, Jackson et al. Citation1987, Banasik et al. Citation1988, Epifanio et al. Citation1991, McConkey et al. Citation1997, Santos and Canino Citation1997, Erskine et al. Citation2002, Khajehie et al. Citation2002, Mahmoudzadeh et al. Citation2002, Rezaiifard et al. Citation2002, Cambazoglu and Gogos Citation2004, Chen and Mackay Citation2004, Sadeghi and Mahdavi Citation2004, Sarkhosh et al. Citation2004, Basson Citation2005, Kinnell Citation2005, Porabdullah Citation2005, Appel et al. Citation2006, Ma Citation2006, Varvani et al. Citation2006, Abdulla and Eshtawi Citation2007, Arekhi Citation2007, Jaramillo Citation2007, Sadeghi et al. Citation2007a, Citation2007b, Citation2008, Khaledi Darvishan et al. Citation2009, Kinnell Citation2010, Noor et al. Citation2010).
Although the MUSLE model has provided good results in some areas, review of the correct values and exact variables used and final conclusions of the application are strictly recommended in order to apply the MUSLE model correctly. Further studies and investigations are needed to draw a comprehensive conclusion.
Related Research Data
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