Physically based soil erosion and sediment yield models revisited

Catena 147 (2016) 595–620

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Physically based soil erosion and sediment yield models revisited Ashish Pandey a,⁎, Sushil K. Himanshu a, S.K. Mishra a, Vijay P. Singh b,c a b c

Department of Water Resources Development and Management, IIT, Roorkee 247667, India Department of Biological and Agricultural Engineering, Texas A & M University, TX, USA Zachry Department of Civil Engineering, Texas A & M University, TX, USA

a r t i c l e

i n f o

Article history: Received 3 November 2015 Received in revised form 14 July 2016 Accepted 2 August 2016 Available online xxxx Keywords: Erosion Sediment yield Physical models Spatial scale Temporal scale Watershed

a b s t r a c t A plenty of models exist for study of the soil erosion and sediment yield processes. However, these models vary significantly in terms of their capability and complexity, input requirements, representation of processes, spatial and temporal scale accountability, practical applicability, and types of output they provide. The present study reviews 50 physically based soil erosion and sediment yield models with respect to these factors including shortcomings and strengths. The literature generally suggests the use of models like SWAT, WEPP, AGNPS, ANSWERS and SHETRAN for soil erosion and sediment studies. Most of the developed soil erosion and sediment yield models are capable of simulating soil detachment and sediment delivery processes at hillslope scale; a limited development was found in the field of reservoir siltation and channel erosion processes. The study proposes a guideline for selection of an appropriate model to the reader for a given application or case study. The future research suggested to improve the simulation and prediction capability of physically based soil erosion and sediment yield models, and should focus on incorporation of improved global web based weather database, inclusion of sediment associated water quality and gully erosion simulation module, and improvement in reservoir siltation and channel erosion simulation processes. © 2016 Elsevier B.V. All rights reserved.

Contents 1.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1. Necessity and constraint of models . . . . . . . . . . . . . . . . 1.2. Objectives of the study . . . . . . . . . . . . . . . . . . . . . . 1.3. Benefits of physically-based models . . . . . . . . . . . . . . . . 1.4. Existing model reviews . . . . . . . . . . . . . . . . . . . . . . 2. Modelling approaches . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Model spatiality and temporality . . . . . . . . . . . . . . . . . 2.2. Algorithm/governing equations used . . . . . . . . . . . . . . . . 2.3. Remote sensing and GIS in soil erosion and sediment yield modelling . 3. Review and synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Classification of models . . . . . . . . . . . . . . . . . . . . . . 3.2. Examples of applications of physically based models. . . . . . . . . 3.2.1. SWAT model . . . . . . . . . . . . . . . . . . . . . . 3.2.2. WEPP model . . . . . . . . . . . . . . . . . . . . . . 3.2.3. AGNPS model . . . . . . . . . . . . . . . . . . . . . . 3.2.4. ANSWERS model . . . . . . . . . . . . . . . . . . . . 3.2.5. Shetran model. . . . . . . . . . . . . . . . . . . . . . 3.3. Guidelines for selection of a model and future research . . . . . . . 4. Summary and conclusions . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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⁎ Corresponding author. E-mail addresses: [email protected], [email protected] (A. Pandey), [email protected] (S.K. Himanshu), [email protected], [email protected] (S.K. Mishra), [email protected] (V.P. Singh).

http://dx.doi.org/10.1016/j.catena.2016.08.002 0341-8162/© 2016 Elsevier B.V. All rights reserved.

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1. Introduction Soil erosion is a major concern for environment and natural resources leading to the reduction in field productivity and soil quality resulting to land degradation. The process of soil erosion includes removal of soil material from one location via natural erosive agents such as water, wind, ice, waves and bioturbation or human-induced erosive agents such as ploughing, fertilizing, overgrazing, building, fires, off road vehicles etc. and their transportation to another location where it is deposited. Thus, erosive agents influence the process of detachment, transportation, and deposition of soil materials (Foster and Meyer, 1972). About 0.3–0.8% (2–12 million hectares) of the world's arable land is affected by excessive soil degradation every year making soil unsuitable for agricultural production (den Biggelaar et al., 2004a). According to den Biggelaar et al. (2004b), there will be additional requirement of 200 million ha of cropped area to feed the increasing population over the next 30 years. Thus to meet the world's future needs good management to protect the soil against further degradation is critical. Soil erosion is mainly affected by natural factors, such as climate, soil, topography, vegetation and anthropogenic activities, such as soil conservation measures and tillage systems (Kuznetsov et al., 1998). Surface sealing and crusts significantly decrease infiltration, and increase runoff and erosion (Moore and Singer, 1990). Erosion is also increased by the soil water repellency i.e., hydrophobicity (Pires et al., 2006). Crucial information about erosion patterns and trends can be obtained by modelling of water-induced soil erosion which allows scenario analysis in relation to current or potential land uses (Millington, 1986). With the development of algorithm and computational capabilities supported with newly available distributed databases, like high resolution digital elevation models (DEM), radar rainfall, remotely sensed satellite data and space technology, a number of models have been developed and these are available for applications to a variety of water resource problems. Soil erosion (water-induced) research was started in early 20th century when it was identified as severe problem in the United States (Chapline, 1929). Application of equations and models for soil erosion prediction started when a relationship between water-induced soil erosion and land slope and length was developed by Austin Zingg (Zingg, 1940), followed shortly by a relationship developed by Smith (1941) that expanded this equation to incorporate conservation practices. Expansion of this work along with large experimental plot data, formed the basis for Universal Soil Loss Equation (USLE), perhaps the paramount achievement in the field of soil erosion modelling (Wischmeier and Smith, 1965, 1978). Stanford Watershed Model (SWM) was probably the first physical model developed in 1966 capable of modelling the entire hydrologic cycle and the entire watershed (Crawford and Linsley, 1966), which was later modified as Hydrological Simulation ProgramFortran (HSPF) by incorporating Fortran version and adding waterquality processes (Bicknell et al., 1993). 1.1. Necessity and constraint of models It is difficult to describe the rate of soil erosion in the watershed over spatial and time scales due to limitations in the field measurements for each part of the watershed. In order to ensure that measurements are not biased by a few years of abnormally high rainfall or an extreme event, long-term measurements are required to build a sufficient data base. Long-term measurements are also needed in order to investigate the response of erosion rates to alterations in climate and land use or the efficiency of erosion control measures. To counter these difficulties, computer based physical models can be used for erosion prediction over a wide range of conditions. To ensure model validity, simulation results can be compared with field measurements. Although, before validation, practically these models also require model calibration with field data and then validated model can be used for simulation of erosion in other areas of similar conditions. Models can only work when they are

applied to conditions (correct spatial and temporal scales considering model accountability for erosion processes) for which they have been calibrated and, if possible, validated (Govers, 2011). A desirable model should satisfy the requirements of universal acceptability; reliability; robustness in nature; ease in use with a minimum of data; and ability to take account of changes in land use, climate and conservation practices. 1.2. Objectives of the study The main objective of this study is to identify and review the most popular physically based soil erosion and sediment yield models and their applications in different parts of the world for performance evaluation, considering: (a) identification and brief description of existing popular physically based soil erosion and sediment yield models encompassing model developer(s)/author(s), year of development/ study, input variables required, governing equation(s) used, future development ideas, capability, shortcoming and strength of the model; (b) description of algorithm or governing equations used in the models; (c) classification of models on the basis of space and time domains, scale, model accountability and their potential for integration with Geographic Information System (GIS); and (d) presentation of a few available case studies from different parts of the world, including information on study area and catchment/watershed size, land use, topography, purpose of the study, reference data used, method(s) used for performance evaluation, sensitivity analysis and findings of the study. It is believed that this study will be helpful in the selection of a suitable physically based model according to the problem at hand, conditions or situations. The current study is restricted to physically-based models incorporating soil erosion and sediment yield aspects although it is worth noting that practically no models are absolutely physical based, as large number of assumptions and empirical/conceptual procedures were usually considered in mathematical expressions describing individual processes in these models. 1.3. Benefits of physically-based models A number of physically based soil erosion hydrological models have been developed worldwide for prediction of soil erosion and sediment yield although practically no models exist that are 100% physically based. Mathematical expressions describing individual processes in these models are based on and large number of assumptions and consideration of empirical/conceptual approaches. Physically based spatially distributed models can be used to identify critical areas by providing the output at any desired location within the watershed with increased accuracy of simulation compared to empirical or conceptual models. Specifically, when time and money are constraints, it is not possible to estimate soil erosion and sediment yield by considering the entire catchment area/watershed at the same time for implementing erosion control measures. In such a situation, physically based modelling not only helps to identify priority areas on the basis of sediment yield but also helps to evaluate the best management practices (BMPs) for the priority sub-watersheds in a short time and with minimum investments. Erosion and sediment yield models represent a powerful tool to predict the effect of man-induced as well as natural environmental changes and impacts on the sediment dynamics, however potential of most of these models to be applied to evaluate scenarios of changing land use management or climate is not too high (de Vente et al., 2013). The present generation of erosion and sediment yield models vary significantly in data handling, computational requirements and sophistication and are quite diverse and comprehensive. Due to large diversity and quite comprehensive nature of models, there exist a multitude of models to address any practical problem and the same model can be applied to a range of problems. In most cases, models mimic quite well the physics underlying hydrological processes and are also distributed in time and space. The main contributions of

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physically-based models to understand and simulate soil erosion processes in comparison with empirical/conceptual approaches are, i) more accurate extrapolation to different land use; ii) more correct representation of erosion/deposition processes; iii) application to more complex conditions including spatially varying soil properties and surface characteristics; iv) more accurate estimation of erosion/deposition and sediment yield on a single storm event basis (Lane et al., 2001). Nevertheless watershed models still have deficiencies, such as large data requirement, lack of user-friendliness, unclear guidelines for conditions of their applicability, improper measure of reliability and lack of expression of their limitations. 1.4. Existing model reviews In this study, relevant literature on physically based soil erosion and sediment yield models have been reviewed from the perspective of their performance evaluation based on different aspects, such as spatial and temporal scales, input and output variables, governing equation(s) used, shortcomings and strength of the model, future development ideas and model efficiency to simulate hydrological as well as soil erosion and sediment transport processes. In this context, Singh (1995) reviewed 26 popular computer based hydrologic models, Schmidt (2000) 7 physically based soil erosion models, Singh and Frevert (2002) reviewed 23 hydrologic models, Singh and Frevert (2006) 24 watershed models, Aksoy and Kavvas (2005) 18 erosion and sediment transport models for hill-slope and watershed scales which include 13 physically based models, and Jetten et al. (1999) 14 soil erosion models of field and catchment scales, including 11 physically based models. On the basis of geospatial characteristics, Zhang et al. (1996) reviewed modelling approaches to prediction by 6 catchment scale soil erosion models. Merritt et al. (2003) reviewed 17 different types of erosion and sediment transport models in which 15 were either physically based or conceptual. Singh and Woolhiser (2002) provided a comprehensive examination of 70 hydrological models. Similar works on hydrologic and non-point source pollution models include those of Borah and Bera (2003) in which they discussed 11 watershed-scale hydrologic and nonpoint-source pollution models. Karydas et al. (2014) identified 82 water erosion models and classified them into eight categories according to their geospatial characteristics. Dhami and Pandey (2013) reviewed 9 recently developed hydrologic models. Some papers provide a systematic categorization of physically based models along with conceptual and empirical models. It follows that only a limited number of models have been considered for review distinguishing the model features (Singh, 1995; Zhang et al., 1996; Jetten et al., 1999; Schmidt, 2000; Singh and Woolhiser, 2002; Borah and Bera, 2003; Merritt et al., 2003; Aksoy and Kavvas, 2005; Singh and Frevert, 2002, 2006; Karydas et al., 2014; Dhami and Pandey, 2013). 2. Modelling approaches Numerous soil erosion and sediment yield models available in the literature differ in terms of processes considered, complexity, requirement of data and their use. There are several factors which affect the selection of an appropriate model, such as catchment characteristics, input and output variables, data availability, objectives, model capability, model efficiency and hardware requirement. On the basis of application of models in physical process simulation, data dependency of models and model algorithm, models are mainly categorized in three classes: Empirical, conceptual, and physically based. Empirical models are primarily based on observations and data response characterization (Wheater et al., 1993). Compared to conceptual and physically based models, data and computational requirements for such models are less, but these are capable of working with coarser measurements and limited data (Jakeman et al., 1999). On the basis of the processes involved, the models can be further classified as stochastic, deterministic or mixed type (Singh, 1988). Ignorance of

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heterogeneity of catchment characteristics and involvement of unrealistic assumptions about the physics of the catchment area are the main criticisms of empirical models (Wheater et al., 1993). These are blackbox type simple models which relate sediment loss to either rainfall or runoff using a typical relationship: Qs = aQb, where Q is water yield, Qs is sediment yield, and a and b are constants. In conceptual models, a catchment is represented as a series of internal storages. Without including the specific details of process interactions, which require detailed catchment information, the model tends to include a general description of catchment processes (Sorooshian, 1991). Parameters of conceptual models have limited physical interpretability. These models play an intermediate role between empirical and physically based models (Beck, 1987). Physical models are based on fundamental physical equations and their solutions describe sediment and stream flows in a catchment. These models not only represent the essential mechanism(s) controlling erosion and sediment yield but also consider physical characteristics, such as topography, geology, land use, climate, plant growth and river flow characteristics; although all these characteristics especially plant growth are not accounted by many models. Conservation of mass and momentum equations for flow and conservation of mass equation for sediment are standard equations used in these model formulations. These models require many more input data and parameters for simulation efforts, and are generally over-parameterized. Use of larger number of parameters benefit to yield a better fit of observed data and increase in degree of freedom. Although, it is not necessary that models with larger number of parameters always achieve better results than models with limited number of parameters (Perrin et al., 2001).

2.1. Model spatiality and temporality Most of the erosion and sediment parameters are scale-dependant. There is no fixed trend relation between drainage basin area and sediment yield; estimation of sediment yield based on drainage basin area alone is problematic and information of spatially distributed factors such as climate, land use, soil, topography and dominant erosion processes is required (De Vente et al., 2007). Models are developed over a range of spatial and temporal scales for a wide range of applications. On the basis of spatial scale, models can be classified into field scale and watershed scale models. The field scale models are having single land use, relatively homogeneous soil, slope and geology, spatially uniform rainfall, and single management system. Field scale tends to have surface runoff hydrology and rain drop splash - sheet - interrill erosion and may be shallow lateral flow and erosion that is dominated by rill erosion processes. Following this crude definition the field scale could be a plot with a few square meters to a few square kilometres (approximately area b10 km2). However, the watershed scale models abound in hydrologic literature and the state-of-art of watershed modelling are reasonably advanced, especially when viewed in the context of practical application (Singh, 1989). Singh (1995) further reclassified watershed scale models to small watershed (up to 100 km2 area), medium-size watershed (100–1000 km2) and large watershed (N 1000 km2) models. The weather events usually vary within the watershed, with continuous base flow throughout the year in many climates. These models employ topography, geology, impact of human activities, storage and translatory routing schemes in hydrological simulation, and often divide watershed into smaller homogeneous parts. Watershed scale tends to have surface, lateral and base flow hydrology, and sediment delivery depends on upland rill erosion, channel erosion and sediment transport processes. Plot-scale collected data cannot be applied for development, calibration, and validation of the model at the watershed scale (Boardman, 2006; Verheijen et al., 2009). Studies conducted at a larger geographical scale focus on off-site impacts, whereas studies conducted at a smaller geographical scale focus on on-site impacts of erosion (Ciesiolka and Rose, 1998).

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Spatial scale is an important criterion in the selection of a model, because it plays a significant role in how specific processes are treated in a model. On the basis of degree to which spatial parameters affect the modelling of soil erosion and sediment yield processes, models may be lumped, semi-distributed and distributed. A lumped model, taking no account of spatial variability of processes, boundary conditions, input data, and watershed characteristics, is expressed generally by ordinary differential equations. On the other hand, spatial variability of processes and outputs are reflected by distributed models (Zhang et al., 1996). In between lumped and distributed models, there lies semi-distributed models which divides the catchment into sub-basins and further into portions of reasonably homogeneous hydrological characteristics using hydrological response units (HRUs) or quasistatistical approach or combination of both (Schumann, 1993). Karydas et al. (2014) redefined lumped and spatially distributed models as those models which are capable of producing, single erosion value (no patterns) in a single run and a number of erosion patterns at different locations of the watershed in the single run respectively. Temporal scale of the model is important, because processes of soil erosion and sediment yield may occur at different time scales. Temporal scale also affects data requirement of the model, for example, it is difficult to get data at the time resolution of hours. Based on the temporal scale of application, models can be either event based (single event or multi event) or continuous. 2.2. Algorithm/governing equations used Applicability and performance of a model mainly depend on the equations and algorithm(s) used. The principal approach used in modelling is conservation of mass to simulate water flows and sediment. In general physically based hydrological models are based on the concept of physics using transfer of mass, momentum, and energy as governing equations (Kandel et al., 2004; Doe et al., 1999) which are solved by various numerical methods. A number of algorithms or governing equations have been used in physically based soil erosion and sediment yield modelling. Some of the commonly used algorithms or governing equations are presented in Table 2. The water flow simulations are based on different approximations of the depth-integrated shallow-water equations (Saint-Venant equations). First is the dynamic wave equation, in which the full shallowwater equation is used (Table 2). Due to computationally intensive numerical solutions, dynamic wave equations are not commonly used in watershed models (Singh, 1996). These equations are used on a limited basis in some models, such as in CASC2D, MIKE 11 models; approximations of these equations are used after ignoring certain terms in the momentum equation. Second is diffusive wave equation, in which inertial term is neglected. The continuity equation is expressed as the balance between inflow, including lateral inflow and outflow, and change in storage, whereas simplified momentum equation is expressed as the pressure gradient equal to the difference between the bed slope and energy gradient (Table 2). Lighthill and Whitham (1955) developed kinematic wave theory to describe the movement of flood waves in long rivers. The kinematic wave equations are the simplest form of dynamic wave equations. Most of the hydrological processes accept kinematic wave theory for modelling (Singh, 1996). The kinematic wave equation consists of the continuity and simplest form of momentum equation, ignoring all the acceleration and pressure gradient terms and the flow is supposed to be uniform. The momentum equation is expressed as energy gradient equal to bed slope (Table 2). For most cases of hydrological significance, the kinematic wave solution gives reasonably accurate results (Singh, 2002). The other methods which are used by most of the models for water flow estimation include SCS Curve Number method (SCS, 1985) and Manning's equation (Manning, 1891) (Table 2). The first attempt in the development of most of the physically based soil erosion and sediment yield models employed the empirical

Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1965, 1978). USLE is based on a large amount of data from the United States. The USLE predicts the long-term average annual soil loss, composed of six factors namely, rainfall erosivity factor, soil erodibility factor, length of the slope, slope factor, crop management factor and conservation practices factor (Table 2). Further, USLE was modified for single storm event taking account of runoff characteristics (MUSLE) (Williams and Berndt, 1977) and various advancements incorporated in USLE resulted in the revised USLE (RUSLE) (Knneth et al., 1991; Renard et al., 1991). The basic equations for sediment concentration as well as surface runoff estimation, are mass conservation equations for sediment and flow (Table 2). Most of the equations dealing with soil erosion and sediment transport on hillslopes mainly concentrate on rill and inter-rill (sheet) net detachment and/or deposition by raindrops and overland flow (Foster et al., 1977; Hjelmfelt et al., 1975; Rose et al., 1983a, 1983b; Morgan et al., 1992; Hairsine and Rose, 1992a, 1992b) (Table 2). Hairsine and Rose (1992a, 1992b) considered surface flow and rain-drop impact as the only contributing agent for surface soil erosion. Their model is based on the equation developed by Rose et al. (1983a, 1983b) The Kilinc and Richardson (1973) equation was developed from bare soils for the estimation of sheet and rill erosion which was further modified by Julien et al. (1995) using the USLE factors K, C and P. Julien and Simons (1984) derived a general relationship as a power function of slope and discharge supported by dimensional analysis for upland erosion and sediment transport. Wicks (1988) determined rate of soil loss by leaf drip and raindrop impact. Sediment transport in channels mainly depends on factors such as water flow, particle size, bed geometry, density of water and sediment etc. (Yalin, 1963; Engelhund and Hansen, 1968; Yang, 1973; Bagnold, 1977) (Table 2). Engelhund and Hansen (1968) proposed an algorithm based on fundamental energy transport equation for transport and deposition of sediments on the movable bed. An expression was derived by Yalin (1963) for the sediment transport in channels based on dynamics of the average saltating motion of the particles and dimensional analysis. It is evident from Table 2 that physically based soil erosion and sediment yield models are based on the concept of physics using transfer of mass, momentum, and energy as governing equations (Kandel et al., 2004; Doe et al., 1999) which are solved by various numerical methods. Most of the models, particularly long term soil loss computing models, use concepts or modifications of USLE. In some of the models, viz., WEPP and EROSION-3D, transport and deposition processes are calculated using topographic data input. Most of the soil erosion and sediment yield models are well capable of simulating hillslope erosion; a limited development has been found in the field of reservoir siltation and channel erosion process. Some of the models such as DWSM, KINEROS, PRMS, EUROSEM etc. are using same general approach for simulation of channel erosion as adopted for hillslope erosion. Mathematical expressions describing individual processes in physical based models are based on a large number of assumptions which, in real world, may not be relevant (Dunin, 1975). The governing equations in these physical based models are derived under very specific physical conditions and are based on laboratory or small-scale in-situ field experiments (Beven, 1989). Practically, these equations are used regularly under varying physical conditions at much greater scales. However, the data used practically is usually point source data taken to represent an entire grid cell in the watershed, although equations are derived for use with continuous temporal and spatial data. The practicality of lumping up small scale physics to the spatial grid scale used in many physical based models are subject to question (Beven, 1989). To reduce data requirements and computational burden, simplified mechanics/physics are sometimes used to represent the physics (e.g. Simplified St. Venant equations), which lead to deflection from the physical basis and additional questionability (Pechlivanidis et al., 2011).

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2.3. Remote sensing and GIS in soil erosion and sediment yield modelling Remote Sensing data hold promises for mapping of soil resources and degraded lands in a timely and cost effective manner (Singh, 1994). The use of GIS for water erosion and sediment yield modelling has a number of consequences, such as facilitation of multiple-source data mixing, creation of a computing environment for potential model re-scaling, and increased complexity in data-method relations (Karydas et al., 2014). Rapid parameterization of hydrologic and soil erosion models can be derived using RS and GIS techniques. Land use/ land cover data derived from satellite imageries and their integration with GIS can be used for soil erosion and sediment yield modelling (Jain and Kothyari, 2000; Pandey et al., 2006, 2009a, 2009b; Dabral et al., 2008). DEM is a valuable tool for the topographic parameterization, especially for erosion and drainage analyses, hill-slope hydrology, watersheds, groundwater flow and contaminant transport studies (Walker and Willgoose, 1999; De Vantier and Feldman, 1993; Jenson and Domingue, 1988). DEMs automatically extract topographic variables, such as slope, aspect, stream network, flow direction, basin geometry, etc. It is a fundamental input to spatially distributed models. Various GIS techniques and DEM have been used to extract data for hydrological and soil erosion models (Smith and Vidmar, 1994; Montgomery, 2003; Coveney and Fotheringham, 2011; Himanshu et al., 2013). 3. Review and synthesis Physically based soil erosion and sediment yield models came into existence after the 1970s, when mainframe computers became readily available, and since then, a variety of such models have been developed ranging from very simple to very complex, and new developments are still in progress. The idea behind a physically based model is that it can be extrapolated without problems, however, in reality extrapolation is normally very problematic since apart from the data availability issue all need calibration, these models were developed for certain environmental and physical conditions and hence they cannot be applied directly to other locations. This leads to the modification of existing models and development of new ones (Jetten et al., 2003). Most of these models are found to be continuously upgraded and updated. These models are capable of quantitative estimation and adequate representation of erosion and deposition. The research dealing with soil erosion by water mainly concentrate on rill erosion and sheet (inter-rill) processes; relatively limited studies have been carried out on gully erosion aspects. Measured sheet and rill erosion do not give realistic value of total erosion nor exact redistribution of eroded soil within a field or watershed. The gully erosion significantly contribute to total soil eroded, its redistribution and delivery to watercourses. The soil loss rate by gully erosion is about 10% to 94% of total sediment yield caused by water erosion (Poesen et al., 2003). Several laboratory and field-based data are required for better understanding and modelling of gully erosion processes. The input data and parameters required for gully erosion modelling, such as critical hydraulic conditions for gully initiation, development and infilling under different environmental as well as management practices conditions, critical rain depths leading to gully initiation, location and dimensions of gullies in the landscape, effect of soil type, lithology and land use change on gully characteristics etc. are difficult to estimate. Due to limitations of the various experimental and monitoring approaches used for these studies and associated modelling difficulties, gully erosion module only appears in a few models (Poesen et al., 1998). Generally, change in the dimensions of gully during an event and its incision, effect of climate and land use change on gully erosion processes are not well modelled. Models that incorporate gully erosion processes are AnnAGNPS, CREAMS, EGEM, GLEAMS, LISEM, SHETRAN, WEPP etc. Thus more research is needed for experimental, field monitoring and gully erosion modelling aspects.

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As seen from Table 1, most of the existing soil erosion models were developed for croplands, only a few models, such as RHEM and SPUR, were developed to estimate soil loss specific for rangeland applications. RHEM is derived from the WEPP model by incorporating new equations derived from rangeland data and by removing relationships developed specifically for croplands. Most of the models have limited capability to model sediment transport and deposition processes while going from soil erosion to sediment yield. Further, model applications are commonly available in the literature. Compilation of these models may help in their potential use, accuracies, and uncertainties expected of them for a particular problem, and situations or conditions for which the models are most suitable and directions for their enhancements or new developments. Therefore, a brief description of a few models follows. GUEST, MEDALUS, MULTSED, OPUS, PEPP-HILLFLOW, RHEM, etc. (Table 1) account for position as only spatial property of erosion parameters. In other words, these models estimate soil loss at a specific location without considering the contribution from, or attribution to, any other neighbouring or remote spatial entities. In most of the models, viz., ACTMO, AGNPS, ANSWERS, CASC2D, CREAMS, DWSM, EGEM, EPIC, EROSION-2D/3D, EUROSEM, GLEAMS, IQQM, KINEROS, LASCAM, LISEM, MEFIDIS, MIKE 11, PALMS, PESERA, PRMS, RillGrow, RUNOFF, SHE/SHESED, SHETRAN, SMODERP, SWAT, SWIM, SWRRB, TOPOG, TOPMODEL, WATEM/SEDEM, WEPP, WESP etc., a well-defined soil sediment transport process is established between sources and receptors. These models employ several topological relations (e.g. association, neighbourhood, or proximity) and thus these relations constitute an integral part of the model. Most physical models (Table 1) generally require hydrometeorological, soil, topographical, and land use data as input for their execution. However, besides these data, AGNPS, ANSWERS, APEX, EPIC, CREAMS, GLEAMS, GSSHA, KINEROS, MEDALUS, OPUS, PALMS, PERFECT, SWIM, SMODERP, SWAT, TOPOG, WEPP also require crop management data (Table 1). Some models, such as AGNPS, AnnAGNPS, ANSWERS, ANSWERS-continuous, CREAMS, DWSM, GLEAMS, HSPF, IDEAL, LASCAM, MIKE 11, OPUS, PRMS, SWAT, SWIM, WEPP etc. include all the three major components, viz., hydrological, sediment, and chemical, in watershed simulation. EPIC, EROSION-2D/3D, EUROSEM, KINEROS, WATEM etc. models were however developed exclusively for simulation and prediction of the erosion process (Table 1). EPIC, EUROSEM, KINEROS, OPUS, SPUR, SWAT, WEPP etc. require a very large number of input parameters for execution and most of these parameters are determined from field investigations. Their determination being very costly, these models are quite difficult to employ in field. 3.1. Classification of models Spatial heterogeneity in topography, soil properties, vegetation and land use including hydro-meteorological drivers are the main factors which affect soil erosion and sediment yield. Remote sensing and GIS techniques have become a valuable tool for estimation of required parameters for spatially distributed soil erosion and sediment yield modelling. The models with GIS integration capability are widely preferred for the works of significant importance. Now-a-days, distributed erosion models are being developed due to enhanced capabilities of GIS platforms. Table 3 classifies the available models on the basis of space and time domains, scale, model accountability and potential of model for integration with GIS. There are many field-scale models (area approximately b10 km2), such as ACTMO, APEX, CREAMS, EGEM, EPIC, EUROSEM, GLEAMS, GUEST, MEDALUS, MULTSED, OPUS, PALMS, PEPP-HILLFLOW, PERFECT, RHEM, RillGrow, SMODERP, SPUR etc., to simulate sediment yield, runoff, and nutrient loss (Table 3). AGNPS, AnnAGNPS, ANSWERS, ANSWERS-continuous, CASC2D, DWSM, EROSION-2D/3D, GAMES, GSSHA, HSPF, HYPE, IDEAL, KINEROS, LASCAM, LISEM,

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Table 1 Physically based soil erosion and sediment yield models. Model name

Developer(s)/author(s) [Year] Input variables

Governing equations used for soil erosion and sediment yield modelling

Model capability

Shortcomings of the model

Strengths of the model

Remarks

Future developments ideas

1.

ACTMO (Agricultural Chemical Transport Model)

Frere et al. (1975)

Climate, soil, chemical, watershed characteristics

USLE

Simulation of hydrology, erosion, chemical

Describe the movement of chemicals in and across an agricultural watershed

Lumped, event based, farm scale model.

Need to develop an improved global web based GIS tool and implementation of user friendly graphics

2.

AGNPS (Agricultural Non-point Source model)

Young et al. (1989)

Climate, soil, topography, land use

USLE for Rainfall detachment, Steady state continuity equation

Capable of estimating non-point source pollution, accurate and flexibility in use

Distributed, event based, watershed scale model

Inclusion of ability to simulate nutrients and pesticides; Improved to AnnAGNPS

3.

AnnAGNPS (Annualized Agricultural Non-point Source model)

Bingner et al. (2011)

Climate, soil, topography, channel, cultural practices

RUSLE, Modified Einstein deposition equation, Bagnold transport equation

Extensive input data requirements; no mass balance calculations; no allowance for spatially variable rainfall

Tool for comparison of effects of implementing various conservation practices and capable of gully erosion simulation

Continuous simulation, watershed scale model

Expansion of ground water module, snow-melt component, weather generator

4.

ANSWERS (Areal Nonpoint Source watershed Environment Response Simulation)

Beasley et al. (1980)

Climate, soil, topography, land use, drainage network, BMPs

USLE for Rainfall detachment, Modified Yalin equation and steady-state sediment continuity equation for sediment transport and deposition

Soil erosion, sediment yield, runoff, peak runoff rate based on storm event having one step as storm duration Long term sediment yield and nutrient transport, hydrology resulting from snow melt, precipitation and irrigation using daily or sub-daily time steps Runoff, peak runoff rate, soil erosion, sediment yield, nutrient simulation based on storm event

Rely on single storm event; low potential for integration with GIS; requirement of large number of input data Rely on single storm event; data intensive; smaller scale of application (up to 25 km2); sub-surface flow cannot be simulated

Capable of estimating non-point source pollution along with soil erosion and sediment yield

Distributed, deterministic, event based, watershed scale simulation model

Need to develop improved global web based GIS tool; consideration of erodibility as time variable parameter; Improved to ANSWERS-continuous

5.

ANSWERS-continuous (Areal Nonpoint Source Watershed Environment Response Simulation-Continuous)

Bouraoui and Dillaha (1996); Bouraoui et al. (2002)

Soil, land use, topography, drainage network, cultural practices

USLE for Rainfall detachment, Modified Yalin's equation for transport and sediment deposition

Rely on single storm event; data intensive; smaller scale of application (up to 25 km2); no sub-surface flow component; erodibility considered as relatively time constant parameter No simulation of reservoir flow and channel sediment

Tool for comparison of effects of implementing various conservation practices

Continuous, process-oriented, distributed, simulation model

Incorporation of gully erosion, in-stream sediment and sediment associated water quality components

6.

APEX (Agricultural Policy/Environmental eXtender) [EPIC model extension]

Williams and Izaurralde (2006)

Climate, crop, watershed characteristics

USLE, MUSLE, RUSLE along with their modifications

Capable of evaluating management scenarios and complex landscape

Continuous simulation, farm scale or small watershed model

Improvements in evaluating climate change impact on crop and GIS integration potential

Long term hydrologic, sediment, nutrient simulation having dual time steps with daily for dry days and 30 s for days with precipitation. Simulation of erosion, land management strategy, water supply and quality, soil quality, weather, plant competition, pests using

Suitable only for field scale and small catchments; less developed sub-surface drainage, water table fluctuation routine, grazing component

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Sr. No.

7.

CASC2D (CASCade of planes in 2-Dimensions)

8.

Julien and Saghafian (1991)

Modified Kilinc-Richard-son equation with USLE factors and conservation of mass for overland sediment, Yang's unit stream power method for channel sediment

CREAMS Knisel (1980) (Chemicals, Runoff and Erosion from Agricultural Management Systems)

Climate, land use, cultural practices

MUSLE

9.

DWSM (Dynamic Watershed Simulation Model)

Borah et al. (1999), Borah and Bera (2000)

Hydrologic, water quality, land use, biological data

Analytical solution of temporary and spatially varying continuity equation

10.

EGEM (Ephemeral Gully Erosion Model)

Watson et al. (1986)

CREAMS empirical relationship, physical process equations

11.

EPIC (Erosion Productivity Impact Calculator)

Williams et al. (1984)

Rainfall, soil, watershed characteristics, identification information Climate, soil, cultural practices

USLE and MUSLE along with their modifications

12.

EROSION-2D/3D

Schmidt (1991); Werner (1995)

Rainfall, soil, topography

Mass balance equation, sediment transport capacity

13.

EUROSEM (European Soil Erosion Model)

Morgan et al. (1993)

Climate, soil, land use, topography

Dynamic mass balance equation of erosion

14.

GAMES (Guelph Model for evaluating the effects of Agricultural Management Systems on Erosion and

Rudra et al. (1986)

Climate, soil, topography, land use

USLE, micro delivery ratio function

Prediction of erosion, deposition, runoff, chemical transport from agricultural land Simulation of surface and sub-surface runoff, flood waves, erosion, sediment yield and agrochemical transport Event based or average annual estimation of ephemeral gully erosion. Simulation of hydrology, weather, erosion, nutrients, plant growth, tillage, economics, soil temperature, plant environment control using daily time step Simulation of erosion on slopes (EROSION-2D) and small catchment (EROSION-3D) based on single rainfall events Simulation of runoff, sediment yield , erosion and deposition based on single rainfall events Estimation of soil loss, rate of erosion, sediment yield

Rely on single storm Suited for both urban and event; no sub-surface agriculture watersheds flow; no simulation of reservoir flow and channel sediment

Unsteady, distributed, event based Hortonian simulation model

Improvement in the ability to simulate nutrients and pesticides; to develop improved global web based GIS tool; improved to GSSHA Modification of the assumption of homogeneity and inclusion of in-stream sediment simulation component

Rely on single storm event; suitable only for field scale and small catchments and low potential for integration with GIS

In addition to overland Lumped, erosion sources, incorporates process-oriented, gully erosion and deposition Field-scale model

Rely on single storm event; long computing time; low robustness and uncertainties of the input data

agrochemical mixing-transport and soil/sediment erosion-transport-deposition capability

Process-oriented, single storm event based, distributed simulation model

To develop improved global web based GIS tool, and inclusion of ability to simulate nutrients and pesticides

Suitable only for field scale and small catchments, and entails detailed gully parameters Suitable only for field scale and small catchments; low potential for integration with GIS

Specially developed for modelling ephemeral gully erosion

Event based Ephemeral Gully Erosion Model.

Can be used effectively to assess the effect of management strategies on soil productivity

Process-oriented, Lumped, field scale continuous simulation model

Inclusion of ability to simulate nutrients and pesticides; needs to Test the model Globally Need to develop improved global web based GIS tool and incorporation of gully erosion components

Rely on single storm event and demand extremely high computational effort

Suitable for large scale simulations

Single storm event based, simulation model

Need to improve the ability to simulate runoff, nutrients and pesticides, and incorporation of gully erosion components

Rely on single storm event; suitable only for field scale and small catchments

Suitable tool for selection of soil protection measures and assessment of erosion risk

Process-oriented, single event, dynamic distributed model

Inclusion of sediment associated water quality and gully component

No gully erosion, nutrients and pesticides simulation component

Can be used with little or no calibration and requires relatively limited data

Soil Erosion and Sedimentation yield model

Need to develop improved global web based GIS tool, and Inclusion of ability to

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Climate, soil, topography, land use

daily time steps usually Simulation of hydrology, weather, soil moisture, erosion, sediment yield

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Table 1 (continued) Sr. No.

Model name

Developer(s)/author(s) [Year] Input variables

Governing equations used for Model soil erosion and sediment yield capability modelling

Shortcomings of the model

Strengths of the model

Remarks

sedimentation)

GLEAMS (Groundwater Loading Effects of Agricultural Management Systems modelling system)

Leonard et al. (1987), Knisel et al. (1993)

Climate, soil, land use, cultural practices

MUSLE

16.

GSSHA (Gridded Surface Subsurface hydrologic Analysis)

Downer and Ogden (2004)

Climate, soil, land use, overland flow data, vegetation cover map

Modified Kilinc-Richard-son equation with USLE factors, one dimensional solution of Richard's equation

17.

GUEST (Griffith University Erosion System Template)

Misra and Rose (1996)

Climate, soil, Transport and Deposition runoff, topography equation

18.

HYPE (Hydrological Predictions for the environment)

Lindstrom et al. (2010)

Climate, soil, land use, topography

Land use and soil type based empirical and conceptual equations

19.

IDEAL (Integrated Design and Evaluation of loading Models)

Barfield et al. (2006)

Climate, soil, land cover

MUSLE, Event mean concentrations and runoff volume

20.

IQQM (Integrated Water quality and quantity model)

DLWC (1995), Simons et al. (1996)

Climate, topography, land use, catchment characteristics

Sediment Continuity equation

21.

KINEROS (KINematic runoff and EROSion model)

Woolhiser et al. (1990)

Climate, soil, topography, vegetation cover, channel geometry

Mass balance equation, sediment transport capacity

22.

LASCAM (Large Scale Catchment Model)

Viney and Sivapalan (1999)

USLE, Stream sediment capacity

23.

LISEM (LImburg Soil Erosion Model)

De Roo et al. (1996a, 1996b)

Climate, topography, land use, catchment characteristics, streamflow and sediment record Climate, soil, land use, erosion/deposition

Generalizederosion-deposition mass balance

Simulation of hydrology, erosion, sediment yield, pesticides and plant nutrients Continuous and event based simulation of hydrology, erosion, sediment yield and nutrients Simulation of runoff, temporal fluctuation of sediment concentration Multi-basin hydrologic, sediment, chemical simulation using daily time step Modelling of storm water BMPs, hydrology, sediment, nutrients, bacteria Simulation of hydrology, water quality (sediment module added later). Runoff, peak runoff rate, soil erosion, sediment yield simulation based on storm event Long term simulation of hydrology, erosion, sediment yield, nutrient Simulation of runoff and sediment yield

Suitable only for field scale and small catchments

No sub-surface flow simulation component

User friendly and can simulate the relative effects of agricultural management practices on water quality and incorporates gully erosion component Suited for both urban and agriculture watersheds

Lumped, process-oriented, event based, Field-scale model

Process-oriented, distributed simulation model

simulate gully erosion, nutrients and pesticides Need to develop improved global web based GIS tool; improvement into parameter estimation and model validation Need to incorporate gully erosion components and to decrease heavily dependency of model on calibration against field data for parameterization Incorporation of In-stream sediment and sediment associated water quality component

Rely on single storm event; field scale model; large data requirement and low potential for GIS integration Lesser user friendly and dependency of model on calibration against field data for parameterization

Interpretation of temporal fluctuations in the sediment concentration from bare soil

Process-oriented, steady state, event based, soil erosion model

Capable of large scale multi-basin and Continental modelling

Process-oriented, semi-distributed, Continuous simulation model

Refinement of evapotranspiration algorithm and hypothesis of glacier/snow processes

Rely on single storm event; low potential for integration with GIS

Capable of predicting pollutant loads and runoff rates through BMPs

Process-oriented, simulation model

Inclusion of expert system for model calibration and to develop improved global web based GIS tool

Risk of over-parametrization and uncertainties of the input data

Can be used as a tool for evaluation and planning water resource management policies at river basin scale

Event based, watershed scale model.

Expansion of application of sediment and water quality module

Rely on single storm Model can be used for event; no sub-surface prediction of runoff from flow and chemical ungauged watersheds simulation component

Process-oriented, single storm event based, distributed simulation model

Inclusion of ability to simulate nutrients and pesticides

Low quality of sediment and nutrient predictions during calibration

Requirement of smaller number of parameters for calibration

Distributed, continuous model

Need to develop improved global web based GIS tool and incorporation of gully erosion component

Rely on single storm event; requirement of extensive model

Completely incorporated in a Single storm event GIS and capable of gully based, distributed erosion simulation; simulation model

Need to incorporate sediment associated water quality

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15.

Future developments ideas

maps, catchment map

based on individual rainfall events with time step of 1 s to 15 min Simulation of hydrology, erosion, atmosphere, plant growth

simulates the effect of soil crust on the processes of runoff generation

components and implementation of user friendly graphics

Rely on single storm event, and is suitable only for field scale and small catchments; requirement of more recent data Rely on single storm event; no chemical simulation component and low potential for GIS integration

Can assess the intensity, extent and severity of desertification process

Process-oriented, event based, Hillslope field scale model

Need to improve ability to simulate nutrients and pesticides; implementation of user friendly graphics

Tool for prediction of the effects of storm change characteristics for soil erosion, runoff and peak runoff rates for the same land-use and watershed characteristics Includes erosion and deposition of both non-cohesive and cohesive sediments

Deterministic, spatially distributed, time dynamic model

Incorporation of sediment associated water quality and gully component

Watershed scale, dynamic computer model

Need to incorporate bank erosion and gully erosion processes

Can estimate sediment yield and runoff from small ungauged watersheds

Distributed, deterministic, Single event based simulation model

Suitable only for field scale and small catchments; consider different uncertainties and assumptions in the estimation of model parameter Rely on single storm event; field scale model; simulation of nutrients and pesticides is not considered

Simulation of chemical transport and water movement in the soil profile

Continuous Field-scale, simulation model

To develop improved global web based GIS tool; need to incorporate ability to simulate nutrients and pesticides Need to develop improved global web based GIS tool and implementation of user friendly graphics

Provide information to farmers to take management decisions

Process-oriented, Event based, distributed, landscape model.

24.

MEDALUS (Mediterranean Desertification and Land Use research programme Model)

Kirkby et al. (1993); Kirkby (1998)

Climate, soil, vegetation, topography

Erosion transport Equation

25.

MEFIDIS (Modelo de ErosaoFIsico e DIStribuido)

Nunes et al. (2006a)

Climate, soil, land use, topography, channel section

Kinetic rainfall energy approach, sediment transport capacity approach

Simulation of runoff, soil erosion based on extreme rainfall events

26.

MIKE 11

MIKE (1995); Hanley et al. (1998)

Climate, topography, land use, catchment characteristics, streamflow and sediment record

Sediment Continuity equation

Hydrology, sediment and water quality simulation, dam break analysis, reservoir planning

27.

MULTSED (MULTiple watershed storm water and SEDiment runoff Simulation model)

Melching and Wenzel (1985)

Rainfall, soil, topography

Sediment Continuity equation, sediment transport capacity

Storm runoff and sediment simulation

28.

OPUS

Smith (1992); Ferreira and Smith (1992)

Climate, soil, crop characteristics, drains

SCS Curve Number method, MUSLE

Simulation of hydrology, erosion, pesticides, nutrients and crop growth

29.

PALMS (Precision Agricultural Landscape Modelling System)

Bonilla et al. (2008)

Climate, soil, crop, surface mask, topography

MUSLE

Simulation of runoff, erosion, soil-plant and atmosphere relation.

30.

PEPP-HILLFLOW (Process orientated Erosion Prediction Program)

Schramm (1994); Bronstert (1994)

Climate, soil, land cover, nutrient

Sediment continuity equation, sediment transport capacity

Simulation of transport of water, soil erosion, fertilizer

Rely on single storm event, and suitable for field scale and small catchments

Includes preferential flow in the macro-pores and can describe transport processes of water, eroded soil and fertilizer

Distributed, process oriented, slope erosion model

31.

PERFECT (Productivity, Erosion and Runoff, Functions to Evaluate Conservation Techniques)

Littleboy et al. (1992)

Climate, soil, crop, tillage

MUSLE

Simulation of hydrology, erosion, crop yield based on daily time steps

Low potential for integration with GIS; suitable for field scale and small catchments; detailed information on tillage practices and

Potential tool for assessing conservation cropping options

Mix of empirical, conceptual and physics based field scale model

Requirement of extensive model data and physical parameter; application of 1-dimensional equations to represent 3-dimensional processes Rely on single storm event, and is suitable only for field scale and small catchments

A. Pandey et al. / Catena 147 (2016) 595–620

data and physical parameter

Further improvements of irrigation routines and crop models; improvement into parameter estimation and model validation Need to develop improved global web based GIS tool and inclusion of expert system for model calibration Inclusion of rainfall intensity in erosion component of the model; incorporation of gully erosion and in-stream sediment components

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Table 1 (continued) Sr. No.

Model name

Developer(s)/author(s) [Year] Input variables

Governing equations used for Model soil erosion and sediment yield capability modelling

PESERA (Pan-European Soil Erosion Risk Assessment)

Kirkby et al. (2004)

Climate, soil, land cover, topography

Sediment transport equation

33.

PRMS (Precipitation Runoff Modelling System)

Leavesley et al. (1983)

Climate, land use, topography

Sediment Continuity equation

34.

RHEM (Rangeland Hydrology and Erosion Model)

Nearing et al. (2011)

Climate, soil, land cover, topography

Splash erosion and transport equation

35.

RillGrow

Favis-Mortlock (1996); Favis-Mortlock et al. (1998)

Climate, DEM

S-Curve (Logistic) stream power based expression

36.

RUNOFF

Borah (1989)

Climate, soil, topography, land use, channel

Flow detachment and raindrop detachment

37.

SEDIMOT (Sedimentology by Distributed Modelling Technique-Version III)

Barfield et al. (1996)

Precipitation, watershed characteristics

SLOSS Routing for sediment yield; CREAMS model method for rill and inter-rill components

38.

SHE/SHESED (SystemeHydrologiqueEuropian/SystemeHydrologiqueEuropian Sediment)

Abbott et al., 1986a, 1986b; Bathurst et al. (1995)

Climate, soil, vegetation, topography, sediment characteristics

Sediment Continuity equation, sediment transport capacity

39.

SHETRAN (SystemeHydrologique Europian-TRANsport)

Ewen et al. (2000)

Climate, soil, land cover, topography

Sediment Continuity equation, sediment transport capacity

40.

SMODERP (Simulation Model of OverlanD Flow and ERosion Process)

Holy et al. (1988)

Rainfall, soil, topography, land

Dynamic concept of erosion.

Simulation of runoff, soil erosion, sediment yield, crop growth at regional scale Daily or storm time scale simulation of general basin hydrology, sediment, stream flow Simulation of runoff, erosion, sediment yield for rangeland

Spatial development and location of hill slope rill systems Simulation of runoff, rainfall excess, erosion, sediment yield

Prediction of runoff and sediment loadings in transition from un-disturbed to disturbed conditions Simulation of surface, subsurface hydrology; Erosion and sediment transport Simulation of runoff, peak runoff rate, erosion, sediment yield and solute transport Overland flow and erosion

Strengths of the model

Remarks

Future developments ideas

Capable of modelling at regional and national scale

Process-oriented, spatially distributed, single storm event based model

Inclusion of ability to simulate nutrients and pesticides

Rely on single storm Well suited in snowmelt event; no sub-surface dominated basins. flow, reservoir sediment and chemical simulation component

Modular design, single storm event based, distributed simulation model

Need to incorporate ability to simulate nutrients and pesticides; to improve global web based GIS tool

Rely on single storm event; suitable only for field scale and small catchments; less potential for simulation of undisturbed rangeland surfaces Rely on single storm event; field scale model and Low potential for integration with GIS Rely on single storm event, and consider different uncertainties and assumptions in model parameter estimates Rely on single storm event, and suitable only for field scale and small catchments

Prediction of runoff and soil loss specific for rangeland (disturbed and undisturbed) applications

Process-oriented, event based, rangeland management model

To produce continuous simulation; inclusion of ability to simulate nutrients and pesticides

Capable of simulating both the initiation of a rill network and its subsequent development. Can simulate sediment on both hillslope as well as in streams

Distributed, process-oriented, Single event based, hillslope rill erosion model Event based, distributed, deterministic model

Advancement in processes like deposition and hydraulics of rill initiation Inclusion of ability to simulate nutrients and pesticides

Effect of storm water and sediment on BMPs

Single event based, field scale model

To increase potential for integration with GIS; improvement into parameter estimation and model validation

No gully erosion, nutrients and pesticides simulation component

Capable of handling large Distributed, quantity of data and Continuous modelling at river basin scale basin-scale, simulation model

Expansion of ability to simulate nutrients and pesticides, and inclusion of expert system for model calibration

Uses very large grids and no simulation of flow through unsaturated zone

Suitable for predicting the climate change and land use impacts; incorporates gully erosion simulation and landslide component

Spatially-distributed, basin-scale, simulation model

Capability to model preferential flow, and improvement into parameter estimation and model validation

Rely on single storm event and suitable

Useful tool for design and decision making in soil and

Single storm event based, simulation

Need to improve parameter estimation

crop management is required Flow routing and chemical simulation component are not fully developed

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32.

Shortcomings of the model

use and vegetation

SPUR (Simulating Production and Utilization of Range Land)

Carlson et al. (1995)

Hydrology, plant, animal, economics

MUSLE, Manning's equation

42.

SWAT (Soil Water Assessment Tool)

Arnold et al. (1998)

Climate, soil, topography, landuse

MUSLE for overland sediment, Bagnold's stream power concept for channel sediment, Continuity equation for reservoir sediment

43.

SWIM (Soil and Water Integrated Model)

Krysanova et al. (1998); Krysanova and Wechsung (2000)

Climate, soil, land cover, crop

MUSLE

44.

SWM [Stanford Watershed Model/Hydrological Simulation Program-Fortran (HSPF)]

Bicknell et al. (1993), Crawford and Linsley (1966)

Climate, soil, land use, topography

Power relation with water storage and flow for overland sediment, cohesive and non-cohesive sediment transport for channel sediment

45.

SWRRB (Simulator for Water Resources in Rural Basins)

Williams et al. (1985)

Rainfall, soil, vegetation

Sediment balance equation, MUSLE

46.

TOPMODEL (TOPography based hydrological MODEL)

Beven and Kirkby (1979)

Hydrologic, soil, topography

Sediment transport capacity

47

TOPOG

Vertessy et al. (1990)

Climate, soil, topography, vegetation

Steady state hydrologic simulation

Simulation of runoff, nutrient, vegetation, erosion using daily time steps Long term hydrologic, sediment, chemical simulation using daily variable constant time steps Simulation of Hydrology, sediment and weather, chemical, plant growth on daily time steps in un-gauged rural basins Simulation of surface, subsurface hydrology; sediment yield and solute transport Simulation of hydrology, erosion, erosion hazard

only for field scale and small catchments

water conservation

model

Suitable only for field scale and small catchments; no chemical simulation component

Suitable to simulate response of prairie ecosystem and rangeland applications

Lumped, continuous, To develop improved field scale, rangeland global web based GIS simulation model tool; inclusion of ability to simulate nutrients and pesticides

Can't simulate floods peak and runoff due to simulation of snow melt for mountainous watersheds efficiently, percolation into a frozen saturated soil layer is based on assumptions Model is quite complicated and no gully erosion simulation component

Integration of the model with GIS interfaces and large databases

Semi-distributed, Continuous simulation model

Enhancements to sediment deposition and stream channel degradation processes; expansion of the plant parameter database, and inclusion of gully erosion component

Capable of water quality and hydrological modelling at river basin scale, and provide reasonable accuracy for ungauged rural basins

Spatially distributed watershed model

To develop improved global web based GIS tool and addition of gully erosion, carbon cycle, lake modules

Requirement of large number of parameters to be calibrated

Model integrates land and soil contaminant, runoff processes simulation with in-stream hydraulic and sediment–chemical interactions

Process-oriented, lumped parameter, continuous simulation model

To decrease heavily dependency of model on calibration against field data for parameterization

Consider different uncertainties and assumptions in model parameter estimates, and requirement of large number of input data and model parameters

Suitable for large complex rural watersheds

Semi-distributed, process-oriented simulation model

Incorporation of gully erosion and land surface sediment components

Suitable only for watersheds having shallow homogeneous soils, moderate topography and no excessive long dry periods Requirement of extensive model data and physical parameter and not

Capability to visualize the results of simulation in a spatial context; requires low level of expertise and few watershed parameters

Distributed, continuous hydrologic, watershed scale simulation model

Incorporation of sediment associated water quality and gully component

Potential tool for estimating spatial distribution of landslide risk indices, erosion hazard

Deterministic, distributed, event based catchment model

Incorporation of gully erosion, in-stream sediment and sediment associated

and model validation; implementation of user friendly graphics

605

(continued on next page)

A. Pandey et al. / Catena 147 (2016) 595–620

41.

simulation from varying intensity of heavy rainfall at the field scale up to 1 km2. Simulation of hydrology, climate, plant and animal interaction on rangeland ecosystem Long term hydrologic, soil erosion, sediment yield, nutrient simulation in large complex watersheds using daily time step

606

Table 1 (continued) Sr. No.

Model name

Developer(s)/author(s) [Year] Input variables

Governing equations used for Model soil erosion and sediment yield capability modelling area identification in complex terrain Simulation of soil erosion, tillage erosion, sedimentation rate

WATEM/SEDEM (Water and Tillage Erosion Model/Sediment Delivery Model)

Oost et al. (2000)

Climate, soil, land cover, flow network map

RUSLE, Mean annual transport capacity

49.

WEPP (Water Erosion Prediction Project)

Laflen et al. (1991)

Climate, soil, topography, cultural practices, channel, impoundment

Steady-state sediment continuity equation

Runoff, soil detachment and deposition, sediment yield on an event or continuous basis using daily time step

50.

WESP (Watershed erosion simulation program)

Lopes (1987)

Climate, soil, topography, channel and watershed characteristic

Unsteady and spatially varying erosion/deposition process

Simulation of runoff, erosion

Strengths of the model

Remarks

able simulation of nutrients and pesticides Suitable only for field scale and small catchments, and detailed information about land cover and high resolution DEM is pre-requisite for good result Requirement of large number of input data and model parameters; inability to simulate the processes occurring in permanent channels such as perennial streams and classical gullies Rely on single storm event, and lack of information on erosion and deposition parameters

Future developments ideas water quality components

Requires limited input data; user-friendly; can simulate effect of soil conservation measures and land use changes for integrated catchment management

Spatially distributed, tillage and water erosion model

Can simulate most of the physical processes vital in soil erosion, including runoff, infiltration, sediment transport, deposition, flow and raindrop detachment, plant growth, and residue decomposition

Process-oriented, distributed, continuous simulation model

Helps in understanding the factors affecting erosion and runoff

Distributed, event based, non-linear, numerical model

Need to incorporate the ability to simulate nutrients and pesticides; to increase sediment transport prediction capability within a river flood plain Expansion of ability to simulate nutrients and pesticides and need to develop improved global web based GIS tool

Need to improve parameter estimation and model validation

A. Pandey et al. / Catena 147 (2016) 595–620

48.

Shortcomings of the model

A. Pandey et al. / Catena 147 (2016) 595–620

MEFIDIS, MIKE 11, PESERA, PRMS, RUNOFF, SEDEM, SHESED, SHETRAN, SWAT, SWIM, SWRRB, TOPMODEL, TOPOG, WEPP, WESP are watershed-scale physical models (Table 3). The WEPP model has the ability to predict the spatial and temporal distributions of soil detachment and deposition on both small field scale (hill-slopes) and large (watershed) scales. AnnAGNPS, ANSWERS-Continuous, APEX, CREAMS, GAMES GLEAMS, HYPE, HSPF, OPUS, PESERA, SEDEM, SPUR, SWIM etc. are continuous simulation models that are useful for analyzing long-term watershed management practices and effects of hydrological changes (Table 3). Single rainfall event based models, such as ACTMO, ANSWERS, AGNPS, DWSM, EGEM, EUROSEM, GUEST, IDEAL, KINEROS, LISEM, MEDALUS, MEFIDIS, MULTSED, PALMS, PEPP-HILLFLOW, PERFECT, RHEM, RillGrow, RUNOFF, SEDIMOT, SHETRAN, SMODERP, TOPOG, WESP etc., are useful for a) evaluating watershed management practices and b) analyzing severe actual or design single-event storms. Some of the models, such as CASC2D, EPIC, GSSHA, IQQM, LASCAM, MIKE 11, PRMS, SHESED, SWAT, SWRRB, TOPMODEL, WEPP etc., have both long term as well as event based simulation capabilities (Table 3).Among the 50 models reviewed, on the basis of temporal scale, 25 models can be classified as event based models, while 13 models as continuous models and 12 models can work on both event and continuous scales. On the basis of spatial scale, 20 models are classified as field-scale models, while 30 models are classified as watershed scale models (i.e., about 40% vs 60%). Among the models reviewed, 12 models are classified as lumped models, and 35 models as distributed models (i.e. about 25% vs 75%). A few models, such as APEX, HSPF, MEDALUS, WEPP, etc., have both lumped and distributed modules (Table 3). Models are distinguished on the basis of space (lumped and distributed) and scale (field and watershed) domains as detailed in Table 3. Most of the models are well capable of simulating hillslope sediments. Models which incorporates channel sediment simulation module, usually having hillslope sediment simulation module except a few such as HYPE, IDEAL, IQQM, MEFIDIS, SEDIMOT, SWRRB, etc. which are limited to channel sediment simulation (Table 3). 3.2. Examples of applications of physically based models Many of the current physically based soil erosion and sediment yield models possess the capability to realistically simulate soil erosion and can be applied to address a wide range of water resources and environmental problems. In order to assess if the model has the capability to produce the required output or not, applications of the selected physically based soil erosion and sediment yield models in different parts of the world were reviewed and these are presented in Table 4. Nash– Sutcliffe efficiency (Nash and Sutcliffe, 1970) is most commonly used method for performance evaluation of models. Co-efficient of determination, co-efficient of correlation and root mean square error are other performance evaluation methods which were used commonly (Table 4). The data used to support the model is an important factor influencing the quality of model predictions. To test the model performance, validation of the calibrated model is essential. To ensure the validity of any model it is essential that simulation results are compared with observed data. Then the validated model can be employed for simulation in other areas of similar conditions. Accurate spatio-temporal distribution of rain-gauge network and availability of other good quality hydrometeorological data are required for reliable hydrological predictions. Most of the watersheds of developing countries are un-gauged or having uneven and sparsely distributed rain-gauge networks. Due to unavailability of long term continuous measurement of hydrometeorological data, most of the models were applied with event based data. Event based models performed better than continuous scale models, primarily because they were operated at smaller time steps. Thus, it can be inferred that input data used to support the

607

model and model parameters are major factors affecting the quality of model predictions. Usually models using more parameters yield better results, but at the cost of increased complexity and diminishing user friendliness, however some studies showing opposite is true due to over parameterisation problems (Jetten et al., 2003). Spatially distributed models are potentially capable to indicate sediment sources and sinks. They require many more input data and calibration efforts, and hence usually having less model accuracy. They are on average found less efficient/effective for sediment yield prediction than spatially lumped models. This indicates that the most complex models do not necessarily provide the most accurate predictions (de Vente et al., 2013). Although, complex models are difficult to handle, usually follow reality. Simpler models, largely based on unrealistic concepts and empirical/dimensionally non-homogeneous equations, exhibit ease in handling. In addition, sensitivity analysis is essential for efficient parameterization. It is used to identify the important model parameters which exert the most influence on model results. Assumptions of any model and model parameter values are subject to error and change, and therefore, sensitivity analysis investigates these errors and potential changes and their effect on the results to be drawn from the model. Sensitivity analysis of different model application analysis reveals that usually runoff is most sensitive to SCS CN values and effective hydraulic conductivity whereas, sensitivity of sediment yield varies greatly for different models (Table 4). Mostly, sediment yield is found sensitive to parameters such as rainfall quantity, crop management factor, saturated hydraulic conductivity, rill and inter-rill erodibility etc. Most of the models were tested for field scale or small watershed applications. Calibration for field scale or small watershed, where the effect of spatial variability on erosion processes strongly influences simulation, is more accurate than for larger watersheds. A few models viz., ANSWERS, DWSM, LASCAM, SHETRAN, SWAT etc. were reported for their application to large watersheds or river basin scale. Incorporation of improved global web based weather database and high integration of model to GIS will improve the model simulation and prediction ability. Hence, model selection should be such that the model application is simple and user friendly. Literature suggests that not all considered models have been widely used and tested. Only a few models, such as SWAT, WEPP, AGNPS ANSWERS and SHETRAN, were found most widely and successfully used for erosion and sediment estimation. Based on reviewed literature applications of some of the popular physical based erosion and sediment yield models are described below. 3.2.1. SWAT model The SWAT model is developed by the USDA Agricultural Research Service (ARS) for predicting the impact of land management practices on hydrology, sediment, and contaminant transport in large river basins over long periods of time (Neitsch et al., 2002). It has proved its applicability on a global scale too, for it has been tested in various countries. SWAT-CUP (SWAT-Calibration and Uncertainty Programs) a decisionmaking framework was developed for calibration of SWAT models which enables sensitivity and uncertainty analysis (Abbaspour, 2007; Arnold et al., 2012). Abbaspour et al. (2015) modelled hydrology of the entire European continent with SWAT and improved SWAT-CUP to include parallel processing and visualization. Fu et al. (2014) revised and tested SWAT model to generate SWAT-CS a version representing hydrological processes dominating forested Canadian Shield catchments. A version SWAT-G was also developed for application to low mountain range catchments of Germany (Eckhardt and Arnold, 2001; Eckhardt et al., 2002). SWAT model was found most versatile model used for different applications such as climate change (Uniyal et al., 2015; Wu et al., 2014); calibration, sensitivity, and/or uncertainty analysis (Abbaspour et al., 2015; Murty et al., 2014; Cibin et al., 2014; Arnold et al., 2012) pollutant cycling/loss and transport (Zhai et al., 2014; Pandey et al., 2005); land use change (Quyen et al., 2014; Schilling et al., 2014; Nie et al., 2011); BMP assessment (Wilson et al., 2014; Piemonti et al., 2013); groundwater and/or soil water impacts (Awan

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Table 2 Governing equations used in different models.

Basic equations for water flow

Name

Algorithm

Dynamic wave equations (Saint-Venant, 1871)

∂h ∂t ∂u ∂t ∂h ∂t ∂h ∂x ∂h ∂t

Diffusive wave equation (Saint-Venant, 1871) Kinematic wave equations (Saint-Venant, 1871

Equations for sediment transport on hillslopes

Equations for sediment transport on hillslopes

Equations for sediment transport in channels

Manning's equation (Manning, 1891) SCS Curve Number (SCS, 1985) Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1965, 1978) Modified Universal Soil Loss Equation (MUSLE) (Williams and Berndt, 1977) Revised Universal Soil Loss Equation (RUSLE) (Knneth et al., 1991; Renard et al., 1991) Foster equation (Foster et al., 1977) Julien and Simons (1984) Kilinc and Richardson (1973) equation Hjelmfelt et al., 1975 (Sediment continuity equation) Bennett mass balance equation (Bennett, 1974) Generalized erosion-deposition mass balance (Morgan et al., 1992) Dynamic Erosion Concept (Holy et al., 1982) Wicks (1988) equation Rose (Rose et al., 1983a, 1983b; Hairsine and Rose, 1992a, 1992b) Bagnold's stream power concept (Bagnold, 1977) Yang's unit stream power method (Yang, 1973) Engelhund and Hansen (1968)

Example model CASCA2D, MIKE 11

¼0

þ u ∂u þ g ∂h ¼ gðS0 −S f Þ ∂x ∂x þ ∂Q ¼q ∂x

CASCA2D, GSSHA, MIKE 11, MEFIDIS, OPUS

¼ S0 −S f

þ ∂Q ¼q ∂x S0 = Sf

CASCA2D, WEPP, OPUS, RHEM, CREAMS, WESP, DWSM, KINEROS, PRMS, MFFIDIS SWAT, ANSWERS, ANSWERS-continuous, CREAMS, SMODERP, OPUS SWAT, AGNPS, AnnAGNPS, EPIC, OPUS, CREAMS, SWRRB, SEDIMOT, SWIM, SWAT AGNPS, ANSWERS, EPIC, SWRRB, APEX, GAMES, ACTMO, LASCAM SWAT, CREAMS, OPUS, GLEAMS, EPIC, SPUR, SEDIMOT, SWRRB, IDEAL, APEX, SWIM, PALMS, PERFECT

1=2

Q ¼ 1n AR2=3 S0 2

ðP−I a Þ Q d ¼ ðP−I a ÞþS

E=RKLSCP Y=a(Q * qp)bRKLSCP

E=RrKrLrSrCrPr

AnnAGNPS, APEX, WATEM/SEDEM

∂qs ∂x

WEPP, AGNPS, ANSWERS, CREAMS, GLEAMS, MEFIDIS, RHEM, PEPP-HILLFLOW CASCA2D CASCA2D, GSSHA

¼ Dr þ Di

qs = αSβ0 qy K qy2:035 0:15 CP qs ¼ 23210S1:664 0 ∂ðC s hÞ ∂t

þ ∂ðC∂xs qÞ ¼ Ds þ D f

∂ ðAC s Þ ∂t

PRMS

∂ þ ∂x ðQ C s Þ−eðx; tÞ ¼ qs ðx; tÞ

KINEROS, OPUS

E = Ds + Df − Dp

LISEM, EUROSEM

a3 a2 DPi,t = a0Ea1 i,t Oi,t TR KiCi

SMODERP

Dr = krFw(1 − Cg − Cr)(Mr + Md) ∂qsi þ ∂ðC∂ts hÞ ¼ Ds þ Dds þ D f þ Ddf ∂s Cmax = Csp × V max

SHETRAN GUEST

þ Rgi −Dp

spexp

SWAT, APEX

∅(Cs, US,U∙, v, w, d) = 0 ðS0 hÞ =2 1 ðs−1Þ2 d50 g =2

CASC2D, GSSHA, PEPP-HILLFLOW

3

qT ¼ 0:04

TC 1=2 ðSGÞdρW τS 1=2

ðS0 qÞ =2 1 ðs−1Þ2 d50 g =2 3

u2 ¼ 0:04ð2gfÞ1=6

TOPOG, CASC2D, SHETRAN, PEPP-HILLFLOW

ANSWERS, ANSWERS-continuous, CREAMS, SHE/SHESED h = depth of flow (m), q = flow per unit width (m3 s−1 m−1), u = velocity of water (m/s), g = acceleration due to gravity (m s−2), S0 = Yalin's equation (Yalin, 1963)

Notations Used

þ

∂q ∂x

¼ 0:635 σ ½1− 1∂ ; ln ð1 þ ∂Þ

bed/surface slope (m m−1), Sf = energy gradient (m m−1), t = time (s), x = longitudinal distance (m), Q = flow rate (m3 s−1), A = flow area (m2), n = Manning's roughness coefficient, R = hydraulic radius (m), Qd = runoff depth (m), P = rainfall (m), S = potential maximum retention after runoff begins (m), Ia = initial abstraction (m), E = average annual soil loss (tons ha−1), R = rainfall erosivity factor, K = soil erodibility factor, L = length of the slope, S = slope factor, C = crop management factor, P = conservation practices factor, Y = sediment yield for an individual storm s ¼sediment rate per unit width of rill channel, Dr = rill net detachment or deposition rate, Di = inter-rill (tones), qp = peak flow rate (m3 s−1), ∂q ∂x net detachment or deposition rate, qs = unit sediment discharge (tons m−1 s−1), e = rate of erosion of the soil bed, E = Erosion, Ds = Detachment by rain drops, Dsd = rainfall re-detachment, Df = detachment by runoff, Ddf = runoff re-entrainment, Dp = Deposition, DPi,t = amount of soil a2 particles detached,Ea1 i, t = kinetic energy of rainfall, Oi,t = overland flow rate, TR = total rainfall duration, i = element of the investigated slope, Dr

= rate of soil detachment, kr = raindrop impact soil erodibility coefficient, Fw = protective effect of surface water layer in reducing the energy imparted to the soil by rain drop and leaf drip impact, Cg = proportion of ground shielded by near ground cover, Cg = proportion of ground shielded by ground level cover, Mr = momentum squared of raindrops reaching the ground per unit time per unit area, Md = momentum squared of leaf drip reaching the ground per unit time per unit area, Rgi = gravity process rate, Cmax = maximum sediment concentration that can be transported by water (kg/L or ton/m3), Vmax = peak channel velocity (m/s), spexp = an exponent defined by user, Cs = total concentration of sand-size sediment particle in motion, unit stream power (L/T), U =shear velocity, v is sediment-water mixture kinematic viscosity, w = fall velocity of sediment, d = particle diameter, qT = amount of transported sediment (m3 m−1 s−1), s = ratio of the specific weight or density of sediment to water, d50 = median grain diameter (m), TC = sediment transport capacity, SG = particle specific gravity, ρW = mass density of water, τS = shear stress acting to detach soil; a0, a1, a2, a3, σ, ∂= empirical parameters; a, b, α, β, γ, Csp = coefficients; Rr, Kr, Lr, Sr, Cr, Pr = Updated USLE parameters with new factor relationships and recent information

and Ismaeel, 2014; Rao and Yang, 2010); evapotranspiration assessment (Wang et al., 2006; Licciardello et al., 2011); snowmelt and/or glacier melt processes (Kang and Lee, 2014; Gan et al., 2015); plant parameters or crop growth/yield (Luo et al., 2008; Qiao et al., 2015; Sun and Ren, 2014); hydropower projects (Piman et al., 2013; Pandey et al., 2015; Wild and Loucks, 2014) etc. In order to identify potential model application problems SWAT-Check was developed to make

modelling applications more reliable and user friendly (White et al., 2014). Although, storm event based high and peak flows are not well simulated by SWAT model, which needs improvement. 3.2.2. WEPP model The WEPP model has been applied widely and it is one of the most extensively used tools for simulation of sediment yield and water

A. Pandey et al. / Catena 147 (2016) 595–620

609

Table 3 Applicability of different models with reference to space, time, and scale/size, accountability to hillslope and channel erosion and their potential for GIS integration (* = Yes, Blank space = No, High = full integration, Medium = partial integration, Low = no integration). Sr. no.

Model acronym

1. 2. 3. 4 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50.

ACTMO AGNPS AnnAGNPS ANSWERS ANSWERS-continuous APEX CASC2D CREAMS DWSM EGEM EPIC EROSION-2D/3D EUROSEM GAMES GLEAMS GSSHA GUEST HYPE IDEAL IQQM KINEROS LASCAM LISEM MEDALUS MEFIDIS MIKE11 MULTSED OPUS PALMS PEPP-HILLFLOW PERFECT PESERA PRMS RHEM RillGrow RUNOFF SEDIMOT SHE/SHESED SHETRAN SMODERP SPUR SWAT SWIM SWM/HSPF SWRRB TOPMODEL TOPOG WATEM/SEDEM WEPP WESP

Space domain

Time domain

Lumped

Continuous

Distributed

*

*

* * * * * *

* * * * * * * * * * * * *

*

* * * * * * * * * * *

* *

*

* * * * * * * *

Event based

Field

* *

*

* * * * * * *

* * *

* * * * * * * *

* * *

* * * * * * * * *

* * *

* * * * * *

* * * * * *

*

* * * * * * * *

Watershed

* * * * * * * *

*

*

Model accountability

* * * *

*

* * * * * * * * * * *

Scale/size

* * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * *

* * *

erosion. Savabi et al. (1995) applied this model using raster-based GIS data. An interface was developed between the WEPP watershed version and Arc-GIS by Cochrane and Flanagan (1999) for small basins. Renschler (2003) developed GeoWEPP with development in techniques to automate the application of WEPP. The model has been tested and evaluated in different geographic locations worldwide by several researchers. WEPP-Water Quality (WEPP-WQ) model was developed for nutrient and pesticide simulation (Savabi et al., 2011). The Water Erosion Prediction Project Climate Assessment Tool (WEPPCAT) is further refinement of WEPP hillslope version for managing and assessing the potential impacts of climate change on sediment loading to streams (Bayley et al., 2010). WEPP offers a number of modules such as WEPPRoad for predicting road erosion and sediment delivery associated to road construction and management; Disturbed-WEPP for prediction of soil erosion after a disturbance by forest operations on a hillside; Rock: Clime to creates climate datasets for use with WEPP or other applications (Elliot, 2004). Pandey et al. (2008) successfully calibrated

* * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * *

*

*

* *

Hillslope sediment

* *

* *

* * * * * * * * * * * *

Channel sediment * *

* * *

* * * * * * * * * * * * * *

*

*

* * * *

* * * * * * *

Potential for integration with GIS

Low High High High High High Low Low Moderate High Low High High Moderate Low High Low Moderate Low Moderate High High High Moderate Low High High High Moderate Moderate Low High High Moderate Low High Low High High High Moderate High High High Moderate High Moderate Moderate Moderate Moderate

and validated the WEPP model for a small Indian (Karso) agricultural watershed to simulate runoff and sediment yield. Further, the tested model was then used for erosion-based prioritization of watershed and evaluation of best management practices (Pandey et al., 2009c). Table 4 shows that the WEPP model more accurately predicts the spatial and temporal distributions of soil detachment and deposition on smaller field scales (hill-slopes) than larger (watershed) scales. It is however recommended for use when data availability is sufficient. Notably, WEPP requires very large number of input parameters, most of them determined from field exhaustive investigations. 3.2.3. AGNPS model The AGNPS model has been evaluated in different environments worldwide mainly for prediction of runoff, peak discharge, sediment load and nutrient discharge. AnnAGNPS is watershed-scale continuous simulation model which is the next generation of single event-based AGNPS model. AGNPS is used mainly to study impact of management

610

Table 4 Details of applications of some selected physical based soil erosion and sediment yield models in different parts of the world. No. Model

Researcher(s) and Region(s) year

Area (ha)

Land use (topography)

Aim

Reference data

Method(s) used Sensitivity analysis for performance evaluation

Results/remarks/conclusion

1.

AGNPS

Cho et al. (2008)

412.5 and 274.1

Agriculture

NA

ICS results were found better than UGS

AGNPS

Jianchang et al. (2008)

95,600

Agriculture

Storm events data of rainfall, surface runoff, peak flow, sediment yield (1993, 1996–97) 10 observed storm data of 2002; long term series data from internal nested small catchment surrounding its catchment

NSE, P-factor, D-factor

2.

Comparison of UGS and ICS of AGNPS for undisturbed forest area Validation of AGNPS for runoff, peak runoff rate, sediment yield, nutrient loss

RE, CP, R

Runoff, peak runoff rate, nutrient most sensitive to SCS CN; sediment most sensitive to rainfall quantity

Satisfactory model performance (R =

Prediction of runoff and sediment loss

17 Observed rainfall, runoff, sediment load, (1994–1996)

R, model co-efficient

NA

Validation of AGNPS for runoff, peak runoff rate, and sediment yield Testing of AGNPS for runoff, peak runoff rate, sediment yield Identification of critical areas and evaluation of BMPs

10 observed storm data of 1989 and 1993 for calibration; 8 observed storm data of 1992 for validation Observed runoff, sediment load, peak runoff, land use (1988 for validation and 1990 for calibration) Observed runoff, sediment load, weather data (1996–2003)

PD

SCS CN values, precipitation and USLE C factors most sensitive parameters NA

Balhan and Banwol watershed, Korea Wuchuan catchment of Jiulong river, China

AGNPS

Chowdary et al. (2004)

Karso Watershed, India

2700

4.

AGNPS

Mohammed et al. (2004)

Kori watershed, Ethiopia

108.2

5.

AGNPS

Haregeweyn and Yohannes (2003)

Augucho catchment, Ethiopia

234

6.

AnnAGNPS

Yuan et al. (2008)

Beasley Lake watershed, Mississippi

915

7.

AnnAGNPS

Polyakov et al. (2007)

Hanalei River 4800 basin, Hawaii

8.

AnnAGNPS

Sarangi et al. (2007)

St Lucia watersheds, British West Indies

4.0 and 7.8

9.

AnnAGNPS

Shrestha et al. (2006)

Masrang Khola watershed, Nepal

130.8

10.

AnnAGNPS

Bisantino et al. (2015)

50,600

11.

ANSWERS

Singh et al. (2006)

Carapelle torrent watershed, Southern Italy Banha Watershed, India

1613

Hilly and undulating agricultural watershed Mostly highlands (1500 above msl) Mostly grass land and arable land Flat land with cotton and soybean as dominant crop Steep mountainous slope and deep valley Agricultural watershed and forested watershed respectively Mountainous with mixed type of land use

R, model efficiency

R, CE, homogeneity test of correlation coefficients Evaluation of Observed runoff, climate data NSE, RE, AnnAGNPS for (2003–2004); sediment load, Willmott index simulation of runoff data (Jan 01, 2004 to Sep. 31, of agreement and soil erosion 2004); 10-m DEM Prediction of runoff Observed runoff, sediment R, CP and sediment loss load, weather, crop management data (July 01, 1999 to Feb. 21, 2000)

NA

Satisfactory model performance on monthly data (model efficiency: 0.73 for runoff; 0.53 for peak runoff rate and 0.90 for sediment yield) Satisfactory model performance (R: 0.59 & 0.58 for runoff; 0.96 & 0.95 for peak runoff rate; 0.97 & 0.97 for sediment yield for 100 and 200 m grids, respectively) Satisfactory model performance (for runoff: NSE = 0.81, RE = 10%, d = 0.94 and sediment: NSE = 0.54, RE = 18%, d = 0.8)

Ground residue cover and canopy cover were most sensitive parameters Runoff and sediment yield most sensitive to SCS CN and unstable aggregate ratio respectively NA

Validation of AnnAGNPS for runoff volume, peak flows, and sediment yield

Observed runoff, sediment load, weather, crop management data (2001 −2002)

CP, RE, R2

Agricultural forested watershed

Prediction of runoff, peak discharge and sediment load

19 storm events observed runoff, peak runoff, sediment load (2007–09)

CP, R2, average error in prediction

Curve Number most sensitive parameter

Flat land with mixed type

Evaluation of ANSWERS using runoff, peak flow, and sediment yield data

31 storm events observed runoff, sediment load, weather, crop management data (1993–1996)

R2, NSE, CRM, RMSE, RE

Runoff and sediment most sensitive to antecedent soil moisture and crop management factor respectively

Satisfactory model performance for runoff and sediment yield (simulation of annual yield most satisfactory followed by monthly and daily) Satisfactory model performance; Can be used as support system for BMPs (0 b CP b 0.25)

Satisfactory performance for runoff volume (R2 = 0.93: validation; 0.91: calibration) than peak flows (R2 = 0.85: validation; 0.77: calibration) and sediment yield (R2 = 0.63: validation; 0.59: calibration) Satisfactory model performance for runoff (R2 = 0.74), peak discharge (R2 = 0.85) and sediment yield (R2 = 0.70)

Satisfactory performance (average percent deviation b 20%)

A. Pandey et al. / Catena 147 (2016) 595–620

3.

0.99&0.98 for runoff, 0.94&0.95 for peak runoff rate of the large catchment and small catchment, respectively; r = 0.76 for sediment, 0.98–0.99 for nutrient of the small catchment) Satisfactory model performance (percent deviation 9.2% to 29% for runoff and 14.2% to 40% for sediment yield from observed)

ANSWERS

Ahmadi et al. (2006)

Watershed located in Shiraz University, Iran Watersheds, Bandi River basin, India

13.

ANSWERS

Sharma and Singh (1995)

14.

ANSWERS, USLE Moehansyah et al. RiamKanan and Adopted (2004) catchment, USLE Indonesia

15.

APEX

16.

CASC2D

17.

CASC2D

Marsik and Waylen (2006)

18

CASC2D

Ogden et al. (2000)

19.

CREAMS

Rekolainen and Posch (1993)

experimental 2 field, Kotkanoja, Jokioinen

Agriculture

20.

DWSM

Gao et al. (2013)

Oneida Creek 31,100 watershed, Central New York

Agricultural urban area

Prediction of runoff and sediment transport

2 storm events observed rainfall, runoff, sediment load (June 28, 2010 and Sep. 09, 2010)

21.

DWSM

Borah et al. (2004)

Three watersheds, Illinois

240,000, 25,000, 10,000

Agriculture

5 storm events observed PE rainfall, runoff, sediment load (Dec 1982 and April 1983; May and June 1998; Sep. 1993)

22.

EGEM

Capra et al. (2005)

Central Sicily, Italy

120

Wheat cultivated

23.

EROSION-3D

Schmidt et al. (1999)

CATSOP watershed, Netherland

42

Agriculture

Simulation of runoff and sediment yield with reservoir routing and a subsurface flow routing schemes Prediction of ephemeral gully erosion Simulation of sediment

24.

EUROSEM

Cai et al. (2005)

Three Gorges Reservoir areas, China

Experimental Steep lands plots (2 m × (slope more than or equal to

3.63

Fallow land

32,682, 45,033, 102,402

Agriculture

102,442

Mountainous with forest as main land use

Mudgal et al. (2012)

Goodwater 35 Creek Watershed 2140 Rojas et al. (2008) Creek experimental watershed, Mississippi Quebrada Estero watershed, Costa Rica Spring Creek watershed, Mississippi

250

2500

Agriculture

Mainly grassland followed by forest and agriculture steep slopes with pastures and forest mainly Urban watershed

Comparison of ANSWERS and revised Yalin's sediment transport equations Prediction of runoff and soil loss

Prediction of soil loss using ANSWERS, USLE and AUSLE and runoff using ANSWERS Development of physically based indices Response of model to different grid cell sizes

Investigation of the hydrologic effect of land use land cover change Investigation of the impact of rain uncertainty on hydrologic predictions To suggest modification for finished condition

Prediction of runoff and erosion rates

11 storm events observed runoff, sediment load, weather data (1993–1998)

CE, PD

NA

Simulated sediment concentration more consistent with original sediment transport equation than Yalin's equation

NA

Under-prediction of total soil loss, sediment, and runoff but within acceptable limit

NA

Performance of ANSWERS model is superior for soil loss prediction but runoff over-predicted

R2, PE

NA

Difference of daily and monthly simulated and observed data PE

NA

Satisfactory simulation of runoff and atrazine. Unsatisfactory sediment yield simulation. Soil erosion best simulated at grid size b150 m

Flash flood hourly and hourly Radar data of July 28, 1997

PE, RMSE

NA

Uncertainty in distribution of rainfall has larger effect on runoff simulations than the watershed characteristics

Observed rainfall, runoff, soil loss (1984 and 1986–89)

Difference of monthly and annual simulated and observed data NSE

NA

Modified model performed better than original one

Model most sensitive to CN as well as flow resistance and flow detachment ability factors NA

Satisfactory model performance (Under prediction of event sediment load and total water volume by 22.3% and 10.7% respectively)

NA

Model not capable of predicting ephemeral gully erosion

Initial soil moisture is most sensitive parameter

Satisfactory runoff prediction but less simulated sediment load prediction

NA

Runoff prediction satisfactory but soil loss prediction unsatisfactory

Storm events observed RMSE, MAE, sediment load (1986); runoff CRM, NSE, R2, estimated using rating curves Goodness of fit, maximum error 2 year (1994–95 and CP, coefficient 1995–96 of Jan. to April) of variation for experimental field data for 4 error in different land uses prediction

Daily observed runoff, sediment, climate and atrazine data (1993–2002) 3 thunderstorm events observed runoff, sediment load, rainfall (1981, 1982 and 1983) 8 storm events rainfall (Aug to Oct., 2001); runoff estimated using rating curves

92 ephemeral gullies data set formed between 1995 and 2000 3 storm events observed rainfall, runoff, sediment load (1987–93) April 1994 data for uncultivated plots and November of 1996, 97, 98

RMSE, NSE, weighted kappa coefficient measured and simulated values difference NA

Model most sensitive to Can be used for BMPs saturated hydraulic conductivity

A. Pandey et al. / Catena 147 (2016) 595–620

12.

Satisfactory performance. It enhanced with reservoir routing and subsurface flow routing

611

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612

Table 4 (continued) No. Model

Researcher(s) and Region(s) year

25.

GLEAMS

Knisel and Turtola (2000)

26.

GSSHA

Sharif et al. (2010)

27.

GSSHA and KINEROS

28.

HSPF

HSPF

Land use (topography)

10 m) 4 plots each of 0.46 ha

47%)

Bull Creek 5500 Watershed, Texas Kalin and Hantush W-2 13.6 (2006) experimental watershed, Lowa Mishra et al. Banha 1695 (2007) Watershed, India

Al-Abed and Al-Sharif (2007)

Zarqa River Basin, Jordan

330,000

Avon River basin, Australia Loess Plateau catchment, China

119 00,000

30.

LASCAM

Viney and Sivapalan (1999)

31.

LISEM

Hessel et al. (2003)

32.

MEFIDIS

Nunes et al. (2005)

Lucky Hills 103 (USA); Ganspoel (Belgium) catchments

Model performed well on multi-sensor rainfall estimates and 30 m grid size

NA

Both models performed satisfactorily

Mixed land use

Simulation of daily runoff and sediment yield

Runoff and sediment yield most sensitive to infiltration capacity index of soil and initial upper zone storage respectively Runoff most sensitive to infiltration capacity index of the soil

Satisfactory model performance (Runoff: NSE = 0.68 during calibration, 0.44–0.67 during validation; Sediment yield: NSE =

Densely populated western hilly areas; desert in southeast basin Flat and dry

Observed daily rainfall, runoff, sediment load of monsoon months (June to Oct. of 1996, 2000 and 2001)

PE, NSE

Runoff simulation on monthly and annual bases

Observed rainfall, runoff (For Calibration: 1988–1991; For validation: 1996–1998)

RMSE,NSE, R2, Percent deviation, t-test

Prediction of stream flow and sediment load Calibration of catchment discharge and erosion pattern Simulation of runoff, peak runoff rate, and erosion patterns

Observed stream flow and sediment load data (1973–19,940) 3 storm events observed rainfall, runoff, sediment load (1988–99)

R2, coefficient of NA variation, average error R, NSE, AUE NA

Prediction of runoff and erosion rates

Observed rainfall, runoff, sediment load (72 months from 18 plots during 1992–98 in Italy and 41

Fontes et al. (2004)

35.

PESERA

Licciardello et al. (2009)

Granja and Ribeirinha experimental basin Plots of Italy (Is Olias) and Netherland (South Limbourg)

0.35 (Granja); 0.183 (Ribeirinha) 0.0020 (Is Olias) and 0.00396 (South Limbourg)

Reynolds Creek basin,

23,400 (data collected

Wicks et al. (1992)

NA

Three hillslopes with different land uses

OPUS

Satisfactory Performance and suitable for BMPs

Simulation of Percent runoff using efficiency, PE different grid sizes Long term 7 storm events observed PE, RMSE simulation of runoff rainfall, runoff, sediment load and sediment yield (between 1975 and 1983)

Observed rainfall, runoff, soil loss (1997–1998)

34.

NA

Partially urbanized with mixed land use Rolling topography

Observed rainfall, runoff, sediment load, drainage, evapotranspiration, N loss, P loss (1987–93) Flood event rain gauge and Radar data of Nov. 17, 2004

Studythe effect of land use on runoff and erosion

12,000

Relative RMSE, MSE

data for other treatment plots

Evaluation of the effect of storm movement on runoff and erosion

Alenquer drainage basin, Portugal

Results/remarks/conclusion

Model testing for runoff, erosion, nutrient losses

Upper reach: forest and scrubland; Rest of the basin: agriculture Agriculture mainly maize and pasture

Nunes et al. (2006b)

Method(s) used Sensitivity analysis for performance evaluation

Agriculture

Waste land followed by cropland and fallow Rangeland and temperate agriculture respectively

MEFIDIS

SHE

Reference data

3500 (total area); 2000 (upstream of weir) 3.7 and 111 respectively

33.

36.

Aim

Rangeland

SHE application to overland flow

16 storm events observed R, R2 rainfall, runoff, sediment load (9 storm events of Lucky Hills 103, 1982–1985; 7 storm events of Ganspoel, 1997–1998) 9 synthetic circular storms; 3 R, NSE, AUE storm events observed rainfall, runoff, sediment load

months from 12 plots during 1986–90 in Netherlands) Measured data of rainfall, runoff, soil loss from rainfall

NA

0.71 during calibration, 0.68–0.90 during validation) Satisfactory model performance (R2 = 0.81 & 0.91 for calibration of monthly and yearly flow respectively)

Both stream flow and sediment load predicted well Unsatisfactory performance due to little resemblance with mapped erosion pattern

Satisfactory performance (R2 = 0.85–0.96); but precision less satisfactory (average unsigned error: 37% to 47%)

NA

Storms moving downstream caused higher net erosion (9.1%) and peak runoff (56.5%) than those moving upstream

R2, NSE, AAE, RMSE

NA

Satisfactory performance; Can be used to study the impact of land use

Ratio of simulated and measured values

NA

Acceptable model performance

R

Sediment yield sensitive to saturated

Possible to calibrate using rainfall simulator data

A. Pandey et al. / Catena 147 (2016) 595–620

29.

Research plots, Finland

Area (ha)

Idaho

from 32.54 m2 rainfall simulator plots) 13,740

37.

SHETRAN

Figueiredo and Bathurst (2007)

Sume basin, Brazil

38.

SWAT

Zabaleta et al. (2014)

Aixola watershed, Spain

480

39.

SWAT

Zhang et al. (2014)

Lixici Watershed, China

69,750

SWAT

Cai et al. (2012)

Upper Huaihe River basin, China

1,019,000

41.

SWAT

Oeurng et al. (2011)

Save catchment, South-west France

111,000

simulator (1982)

Simulation of runoff and sediment yield To investigate the impact of climate change on runoff and sediment yield

Observed rainfall, runoff, soil loss data from basin (1957–80) and micro-basins (1982–91) Daily river flow (Jan 2005-Dec. 2010) and sediment load (Jan 2005 to May 2008)

Agricultural area in Jialing River basin

To investigate the impact of land use change on soil erosion and sediment yield

Daily Precipitation, runoff and sediment yield (1975–1995): Air temperature, wind speed, humidity (1975–1998)

NSE, R2, PB

Mainly of farmland, woodland and paddy field Intensively farmed catchment

To investigate the effect of land use change on daily sediment yield Evaluation of runoff, sediment, and organic carbon yield and identification of critical soil erosion areas To investigate the effect of land use-soil interaction on water and sediment yield Identification of critical areas to soil erosion

Daily runoff, sediment yield, meteorological data (1987–2005), 30 m-DEM

R2, NSE, RE, RMSE, SSQR, p-factor, r-factor R2, NSE, RE, NSE

Mixed type with dominant vegetation cover forested areas in the Cantabrian region

Hilly and

Average annual sediment

SWAT

Setegn et al. (2009)

Lake Tana Watershed, Ethiopia

1,509,600

Sub-basin of Blue Nile River basin with rich biodiversity

Rostamian et al. (2008)

Beheshtabad and Vanak watersheds, Iran

386,000 and 319,800

Mountainous Simulation of daily with agriculture sediment load and followed by monthly runoff pasture rolling and hilly topography

WEPP

Singh et al. (2011)

Umroi watershed, India

239.4

47.

WEPP

Pandey et al.

Karso

2793

CN quite sensitive for prediction of flow and sediment

Prediction of annual erosion w.r.t. land-use change Multi-vegetated To develop BMP watershed plan

43.

46.

Satisfactory but underestimated runoff and sediment yield when used CGCM2 and ECHAM4 models

Runoff most sensitive to evaporation compensation factor and SCS CN values

Mainly of range brush, range grasses and forest

200 and 4700 respectively

Runoff and sediment yield most sensitive to SCS CN values and maximum potential leaf area index respectively Runoff and sediment yield most sensitive to SCS CN values and linear parameters for sediment re-entrainment respectively NA

R2, NSE

117,845

Yangjuangou and Yangou catchments

RE, NSE

Blue Nile basin: Meteorological data (1978–2004), Flow data (1981–2004); Angeri watershed: Flow, meteorological and sediment data (1984–1993) Meteorological data (1985–2004); Flow data (1992–2004: Beheshtabad and1985–2004: Vanak watershed) Soil samples at 58 sites in Yangjuangou; Land-use change between 1990 and 2005 184 storm events of monsoon season 2003 and 2004

Cowhouse Creek watershed, Texas

WATEM/SEDEM Feng et al. (2010)

Satisfactory performance (R2 = 0.80 for runoff and 0.46 for sediment yield)

NA

Wang et al. (2010)

45.

NA

R2, NSE, PB

SWAT

SWAT

R2, PE

Daily meteorological and flow data (Dec 1984-Nov. 2006), no observed sediment data

42.

44.

Daily meteorological and flow data (Jan 1999-March 2009), Sediment (Jan 2007-March 2009), organic carbon (Jan 2008-March 2009)

hydraulic conductivity, erodibility coefficient

Identification of

Satisfactory model performance (R2 = 0.78–0.94 for runoff and R2 = 0.72–0.88 for sediment yield); Model suitable for BMPs

Satisfactory performance (NSE: 0.63–0.79)

Performance less than satisfactory (R2 = 0.56 for runoff and R2 = 0.51 for sediment yield)

Annual sediment and water yield increase in all soils with conversion of range brush to range grass

Performance less than satisfactory (R2 N 0.5; NSE N 0.5)

R2, NSE, RMSE, SDR, PB, P-factor, D-factor

Runoff most sensitive to SCS CN values

Less than satisfactory performance; P factor (0.31–0.86 and 0.71–0.80); D factor (0.3–1.1 and 0.77–1.16) for Beheshtabad and Vanak watershed respectively

R2, NSE, RSR

NA

Poor performance in cell-wise prediction of erosion amounts

t-tests, NSE, Runoff and sediment percent, yield most sensitive to deviation, RMSE effective hydraulic conductivity and rill erodibility respectively t-tests, NSE, PD, NA

A. Pandey et al. / Catena 147 (2016) 595–620

40.

erodibility

WEPP is suitable for implementation of BMPs (results fit at 95% significance level of t-test)

WEPP is suitable for implementation of 613

(continued on next page)

614

Table 4 (continued) No. Model

48.

WEPP

Researcher(s) and Region(s) year

Area (ha)

(2009c)

Watershed, India

Pandey et al. (2008)

Karso Watershed, India

2793

WEPP

Baigorria and Romero (2007)

Andean watershed, Peru

6000

50.

WEPP

Raclot and Albergel (2006)

Kamech catchment, Cap Bon, Tunisia

245

Aim

Reference data

undulating agricultural watershed Hilly and undulating agricultural watershed

critical watersheds and evaluation of BMPs Simulation of daily runoff and sediment yield

yield (years 1992, 1993, 1995 RMSE to 1997, and 2000) and crop parameters Daily monsoon (1992–2000) R2, NSE, PD, t-test data (meteorological and hydrological)

Mainly of Croplands, natural and cultivated pastures Semi-arid Mediterranean agricultural watershed

Identification of areas critical to soil erosion and runoff generation

Climate data (1995–1999)

Daily and annual Daily hydro-meteorological simulation of runoff data of Jan. 1995 to Sept. and sediment yield 2002

Method(s) used Sensitivity analysis for performance evaluation

R2, NSE, PD, t-test

RMSE, NSE, t-test

Results/remarks/conclusion

BMPs

Runoff and sediment yield most sensitive to effective hydraulic conductivity and inter-rill erodibility NA

Satisfactory model performance (R2 = 0.86–0.91 for runoff and R2 = 0.81–0.95 for sediment yield)

GEMSE can be used with WEPP model

Error ranged from 3% - 59% for hydrologic output; not fit for sediment yield (errors N250%)

Note: Abbreviations used. NA: Not Available/Applicable; BMP: Best Management Practices; R2: Co-efficient of determination, R: Co-efficient of Correlation; RE: Relative Error; PE: Percentage error; NSE: Nash-Sutcliffe efficiency, RMSE: Root Mean Square Error; PB: Percent Bias; MAE: Mean Absolute Error; SSQR: Sum of Square Residuals; CP: Performance Co-efficient; CE: coefficient of efficiency; SDR: standard deviation ratio; CRM: coefficient of residual mass; MSE: Mean Square Error; AAR: Average Absolute Error; AUE: Average unsigned error; UGS: Uniform Grid Scheme; ICS: Irregular Cell Based Scheme.

A. Pandey et al. / Catena 147 (2016) 595–620

49.

Land use (topography)

A. Pandey et al. / Catena 147 (2016) 595–620

decisions within a watershed system on water, sediment and chemical loadings. AnnAGNPS is used widely for modelling runoff and sediment yield (Licciardello et al., 2007; Abdelwahab et al., 2014; Chahor et al., 2014); modelling nutrient transport (Baginska et al., 2003); evaluation of BMPs (Srivastava et al., 2002; Dabney et al., 2004; Parker et al., 2007; Qi and Altinakar, 2011); Simulation of ephemeral gully erosion (Taguas et al., 2012; Gordon et al., 2007) etc. TopAGNPS (Garbrecht and Martz, 1995) module is used to process the DEM data, raster processing and raster formatting whereas, AGFlow (Bingner et al., 1997) module of AnnAGNPS is used to generate the DEM related input parameters. Due to its gully erosion simulation capability and high integration with GIS, its popularity has increased tremendously. Emili and Greene (2013) developed GIS protocol and showed its usefulness in pragmatic application of GIS. 3.2.4. ANSWERS model The ANSWERS model has been evaluated in different environments worldwide mainly for prediction of runoff, erosion, sediment load and nutrient discharge. ANSWERS-Continuous model is a watershed-scale continuous simulation model which is the next generation of single event-based ANSWERS model. Worldwide application of ANSWERS model reveals that model predicted runoff rate is generally very close to the observed runoff (Razavian, 1990; Sharma and Singh, 1995; Connolly and Silburn, 1995; Connolly et al., 1997a, 1997b; Garosi, 1997; Bouraoui and Dillaha, 1996; Singh et al., 2006), however most of the above studies reveals that model predicted sediment yield is not very close to the measured values. Sediment yield was underestimated (Breve et al., 1989; Ahmadi et al., 2006) while overestimated (Bhuyan et al., 2002; Walling et al., 2003; Moehansyah et al., 2004), in some watersheds. Sharma and Singh (1995) outlined that underestimation or overestimation of sediment yield was highly ascribed to rainfall intensity, amount of rainfall and soil moisture conditions. 3.2.5. Shetran model SHETRAN model is a spatially-distributed, finite-difference model for water flow and solute/sediment transport in river catchments. It is a successor of SHE model developed at School of Civil Engineering and Geosciences, Newcastle University, England. It has however proved its applicability on a global scale, for it has been tested in various countries. SHETRAN model is used widely for water flow, sediment and nutrient transport including landslide hazard assessment (Bathurst et al., 2006; Miller et al., 2012; Bovolo and Bathurst, 2012); climate and land use change (Bathurst et al., 1996; Ewen and Parkin, 1996; Parkin et al., 1996; Bathurst et al., 2004) groundwater and/or soil water impacts (Parkin and Adams, 1998; Parkin et al., 2007); forest impact on floods and sediments (Bathurst et al., 2011a, 2011b; Birkinshaw et al., 2011) etc. Zhang et al. (2013) automatically calibrated SHETRAN model using Modified Shuffled Complex Evolution (MSCE) technique developed by Santos et al. (2003) in semi-arid Cobres basin of southern Portugal. SHETRAN model is more popular in European countries than others. Some of its applications in India include the works of Jain et al. (1992); Lohani et al. (1993) among others. 3.3. Guidelines for selection of a model and future research Every models have their own performance capabilities in soil erosion and sediment yield modelling and their application depends upon the study objectives and accuracy desired. Based on review work the proper model selection should follow the following, 1) Problem Identification: The first step in modelling is to know the desired output required from the simulation. In order to minimise the risk of using wrong tool for the job problems must be clearly addressed. 2) Model Selection: Before model selection one should know that what kind of system is to be modelled (field or watershed or river basin); elements to be modelled (hillslope sediment or channel sediment on daily/monthly/

615

seasonal/annual basis); spatial variability (lumped or distributed); temporal variability (continuous or event based); skill available; quality, length and time of data available for study; physiographic and climatic condition of the system; cost involved. The criterion for model selection should also include simplicity of its application, accuracy, strength and limitations, parameters consistency and output sensitivity to changes in parameters. The models with GIS integration capability are widely preferred for the works of significant importance. The data available should be sufficient to estimate different parameters of governing equations used for erosion and sediment modelling aspect. 3) Sensitivity/uncertainty analysis and model evaluation: The impact of pertinent uncertainty sources which affects reliability and accuracy of model output must be identified before model evaluation. Sensitivity analysis ensures impact of input parameters on output variables and model performance. To ensure model validity, simulation results can be compared with the field measurements, although before validation, practically these models also require model calibration with field data. 4) Use of accredited/accepted model: The validated model then can be used for simulation of erosion and sediment process in other areas of similar conditions although. All models and their results require a critical look, and also, uncertainties should be considered, quantified and should be taken into consideration when interpreting results. Accurate weather and climate data is required for reliable prediction of hydrological models. Daily precipitation, wind, relative humidity, and solar data can be downloaded from SWAT global web based weather database (http://globalweather.tamu.edu/). There are also some global databases where weather and climate data from different areas of the world are available (https://www.ncdc.noaa.gov/data-access; http:// worldweather.wmo.int/en/home.html; http://gcmd.nasa.gov/learn/ pointers/weather.html). Soil erosion is a serious global threat for the scientific community because of its adverse effect on the environment, agronomic productivity, and its impact on food security and the quality of life. The review suggests to mobilize the scientific community to develop an integrated programme for standards, methods, data collection, and research networks for assessment and monitoring of soil erosion and land degradation. The future research suggested to improve the simulation and prediction capability of physically based soil erosion and sediment yield models, and should focus on incorporation of improved global web based weather database, inclusion of sediment associated water quality and gully erosion simulation module, and improvement in reservoir siltation and channel erosion simulation processes. It is generally recognized that actual quantification and representation of uncertainty in hydrological modelling is crucial for decision making, more research effort should be devoted to develop robust uncertainty frameworks. This review work propose improvements in evaluating hydrological responses in ungauged catchments and interior catchment points, and also to assess the impact of climate/land use change on soil erosion and sediment yield modelling. 4. Summary and conclusions The review papers indicated that most of the models were developed for agricultural catchments and tested in developed countries only, requiring their site specific calibration before application. Event based models performed better than continuous scale models, primarily because they were operated at smaller time steps. Calibration for field scale or small watershed, where the effect of spatial variability on erosion processes strongly influences simulation, is more accurate than for larger watersheds. Input data used to support the model and its parameters are major factors affecting the quality of model predictions. Besides, the major issues facing the practical application of physically based models are the requirement of extensive input data, natural complexity, model complexity, and accuracy. An exhaustive review of worldwide applications of the reviewed models revealed SWAT, WEPP, AGNPS, ANSWERS and SHETRAN models to be the most

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promising ones for simulation of erosion and sediment transport processes, and therefore, these can be better used for implementation of best management practices (BMP). It is however worth indicating that only a few models, such as SPUR and WEPP derived RHEM model, were developed to estimate soil loss specifically for rangeland applications. AnnAGNPS, CREAMS, EGEM, GLEAM, SLISEM, SHETRAN, WEPP, etc. are few of the models incorporating gully erosion. Most of the soil erosion and sediment yield models developed are well capable of simulating hillslope erosion; a limited development was found in the field of reservoir siltation and channel erosion. The present study provides a clear guidelines to the reader to select an appropriate model for a given application and will help to sort out which model should be used in which conditions. The review suggests to mobilize the scientific community to develop an integrated programme for standards, methods, data collection, and research networks for assessment and monitoring of soil erosion and land degradation. The future research suggested to improve the simulation and prediction capability of physically based soil erosion and sediment yield models, and should focus on incorporation of improved global web based weather database, inclusion of sediment associated water quality and gully erosion simulation module, and improvement in reservoir siltation and channel erosion simulation processes. Acknowledgement The authors would like to thank the editor and anonymous referees for contributing insightful remarks and useful suggestions, which led to a substantially improved manuscript. References Abbaspour, K.C., 2007. User Manual for SWAT-CUP, SWAT Calibration and Uncertainty Analysis Programs. Swiss Federal Institute of Aquatic Science and Technology, Eawag, Duebendorf, Switzerland. Abbaspour, K.C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H., Kløve, B., 2015. A continental-scale hydrology and water quality model for Europe: calibration and uncertainty of a high-resolution large-scale SWAT model. J. Hydrol. 524, 733–752. Abbott, M.B., Bathurst, J.C., Cunge, J.A., O'Connell, P.E., Rasmussen, J., 1986a. An introduction to the European hydrologic system-Systeme Hydrologique Europeen, “SHE”, 1: history and philosophy of a physically-based, distributed modelling system. J. Hydrol. 87 (1), 45–59. Abbott, M.B., Bathurst, J.C., Cunge, J.A., O′Connell, P.E., Rasmussen, J., 1986b. An introduction to the European hydrologic system-Systeme Hydrologique Europeen, “SHE”, 2: structure of a physically-based, distributed modeling system. J. Hydrol. 87 (1), 61–77. Abdelwahab, O.M., Bingner, R.L., Milillo, F., Gentile, F., 2014. Effectiveness of alternative management scenarios on the sediment load in a Mediterranean agricultural watershed. J. Agric. Eng. 45 (3), 125–136. Ahmadi, S.H., Amin, S., Reza, K.A., Mirzamostafa, N., 2006. Simulating watershed outlet sediment concentration using the ANSWERS model by applying two sediment transport capacity equations. Biosyst. Eng. 94 (4), 615–626. Aksoy, H., Kavvas, M.L., 2005. A review of hillslope and watershed scale erosion and sediment transport models. Catena 64 (2), 247–271. Al-Abed, N., Al-Sharif, M., 2007. Hydrological modeling of Zarqa River basin – Jordan using the hydrological simulation program – FORTRAN (HSPF) model. Water Resour. Manag. 22 (9), 1203–1220. Arnold, J.G., Srinivasan, R., Muttiah, R.S., Williams, J.R., 1998. Large area hydrologic modeling and assessment. Part I: model development. American Water Resources Association]–>J. Am. Water Resour. Assoc. 34 (1), 73–89. Arnold, J.G., Moriasi, D.N., Gassman, P.W., Abbaspour, K.C., White, M.J., Srinivasan, R., ... Jha, M.K., 2012. SWAT: model use, calibration, and validation. Trans. ASABE 55 (4), 1491–1508. Awan, U.K., Ismaeel, A., 2014. A new technique to map groundwater recharge in irrigated areas using a SWAT model under changing climate. J. Hydrol. 519, 1368–1382. Baginska, B., Milne-Home, W., Cornish, P.S., 2003. Modelling nutrient transport in Currency Creek, NSW with AnnAGNPS and PEST. Environ. Model. Softw. 18 (8), 801–808. Bagnold, R.A., 1977. Bed load transport by natural rivers. Water Resour. Res. 13 (2), 303–312. Baigorria, G.A., Romero, C.C., 2007. Assessment of erosion hotspots in a watershed: integrating the WEPP model and GIS in a case study in the Peruvian Andes. Environ. Model. Softw. 22 (8), 1175–1183. Barfield, J.B., Hayes, J.C., Stevens, E., Harp, S.L., Fogle, A., 1996. Chapter 16: SEDIMOT III Model. In: Singh, V.P., Donald, K.F. (Eds.), Watershed Models. Taylor and Francis, Colo, pp. 381–398. Barfield, J.B., Hayes, J.C., Harp, S.L., Holbrook, K.F., Gillespie, J., 2006. Chapter 15: IDEAL: Integrated Design and Evaluation of Loading Models. In: Singh, V.P., Donald, K.F. (Eds.), Watershed Models. Taylor and Francis, Colo, pp. 361–379.

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