Watershed models for assessment of hydrological behavior of the catchments: a comparative study

Soil loss is a grave issue due to its severe effects on economic productivity and environmental consequences leading to land disintegration. Studies show that 2–15 million hectares of total cultivable land are affected every year due to soil erosion, making it incongruous for any kind of productivity (Den Biggelaar et al. 2004). Soil disintegration is mostly influenced by normal variables; for example, atmosphere, soil, geography, vegetation and anthropogenic exercises, for example, soil protection measures furthermore, culturing frameworks (Kuznetsov et al. 1998).

Watershed behavior and hydrologic cycle are to be wholly examined so as to discover the variability in environmental and economic conditions. The reliable prediction of the various hydrological parameters becomes tedious and time consuming by conventional methods. Currently, many hydrological models have been developed over the world to determine and analyze the effect of land use, climatic conditions and soil characteristics on hydrology. Each watershed model has got its own attributes to carry out the different processes and capability to analyze the parameters. The data utilized by these different and diverse models are precipitation, air temperature, soil properties, geology, vegetation cover, hydrogeology and other physical parameters. Improved comprehension of how each of these elements affect water chemistry and quality requires improved capacities to comprehend fundamental procedures and their influence on water accessibility and use. This involves utilizing all encompassing methodologies which coordinate hydrologic forms at the watershed scale to decide a general watershed reaction to changing atmosphere (Singh & Woolhiser 2002).

Based on the applications of watershed models in analyzing physical parameters, the simulation processes and algorithm, models are primarily classified in three categories named empirical models, conceptual models, and physically based models. Experimental models rely on actual observations and characterizing different data responses with less computational necessities (Wheater et al. 1993). The conceptual model represents a catchment as a progression of internal storages. These models use semi-empirical equations and evaluate the model parameters from field observation as well as by model calibration. These models incorporate a general depiction of the catchment, thereby avoiding point by point data necessities and represent a catchment as a progression of internal storages (Sorooshian 1991). These models fill a gap between empirical and physically based models and thus play a transitional role (Beck 1987).

Physical models are based on solutions of various physical equations to define and analyze the various hydrological parameters like runoff and sediment yield. These models use the various physical characteristics of a catchment like topography, land cover, flow characteristics, soil characteristics and basin geology for the assessment of the hydrological behavior of the catchment. The standards equations used in these models are based on conservation of mass and momentum for flow estimation.

A watershed model estimates various hydrological parameters with a comprehensive methodology compared to other models, which principally center on individual procedures at field-scale without full incorporation of a watershed area (Oogathoo 2006).Watershed models are considered to be the most vital tools in the current era, which assess the hydrological behavior of any catchment and the impact of different changing factors on the hydrology of the catchment. The various models reviewed are summarized in Tables 1 and 3 with the governing equations of these models shown in Table 2.

It is a spatially distributed physical model that analyses the various processes of the hydrological cycle (Ewen et al. 2000). SHETRAN utilizes the Rutter equation (Rutter et al. 1971) to obtain the interference and transpiration is represented by a root density function. Runoff is estimated either by calculating the rainfall excess over infiltration capacity or by analyzing saturation excess. Surface runoff is estimated by routing Saint-Venant equations and transport capacity can be determined either by Yalin's equation (Yalin 1963) or Engelund-Hansen's equation (Engelhund & Hansen 1967).

It is a distributed watershed model capable of estimating the maximum of hydrological parameters (Young et al. 1989) and uses a modified version of the Universal Soil Loss Equation (Wischmeier & Smith 1978) for prediction of soil loss and the Soil Conservation Services runoff curve method for estimating surface runoff. Sediment yield is divided into different classes and the process includes sediment transport and deposition relations depicted by Bagnold (1966), Foster et al. (1981) and Lane (1982). The model also analyses the chemical transport by the procedure given by Frere et al. (1980).

It is a single storm event based distribution model that incorporates kinematic wave equations for estimating surface runoff and mass balance equations along with sediment transport equations for analysis of sediment yield (Woolhiser et al. 1990). KINEROS is improved into KINEROS2, which incorporates a new method for redistribution of soil water during rainfall interruptions and has an infiltration algorithm that can handle a two layer soil profile.

It is an event based analyzing tool designed to help with surveying rangeland preservation practice impacts. It uses semi-analytical solution to the kinematic wave equations for routing runoff. Sediment calculations utilize splash erosion and transportation equations (Nearing et al. 2011).

It is a continuous simulation model that uses semi-empirical equations for estimation of surface runoff and utilizes separate methods for evaluating overland and channel sediment (Crawford & Burges 2004).

It is a semi-distributed model used in complex rural watersheds capable of simulating hydrology and soil loss on daily time steps. Overland flow is evaluated using Soil Conservation Service curve number method (SCS 1985) while Modified Universal Soil Loss Equations (Williams & Berndt 1977) and sediment balance equations are used for sediment calculations. This model was created to analyze the hydrological behavior of complex rural basins (Williams et al. 1985). Improvisations to the model were made for application of large rural basins by allowing simultaneous computations on several sub-basins and with a few other modifications to the CREAMS model (Knisel 1980). Ritchie's evapo-transpiration model (Ritchie 1972) is utilized for estimating evapo-transpiration. Precipitation and temperature data can be simulated using a weather generator (Nicks 1974) if the actual data is unavailable, which makes the model applicable to ungauged rural watersheds.

It is a field-scale model based on single events that uses the SCS curve number method for simulation of runoff and SLOSS routing for sediment estimation. The rill and inter-rill components are calculated using the same methodology as used in the CREAMS model (Barfield et al. 2006).

It is an event based model that relies on a single storm event and is capable of estimating runoff, peak runoff rate and soil loss. It uses the SCS curve number method for simulating surface runoff and Green& Ampt. equation for calculating other hydrological processes. Universal Soil Loss Equation and modified Yalin's equations are used for soil loss and its transportation and deposition (Beasley & Huggins 1982).

It is a distributed watershed model that uses hydrological similarity, probability distribution function, Darcy's law and topographic indices for simulation of overland flow (Beven & Kirkby 1979). The distribution of topographic indices used for hydrologic similarity is explained in detail by Beven et al. (1995).

It is a non-linear numerical model that relies on a single storm event and utilizes unsteady overland flow for kinematic wave approximations and offers Green and Ampt. equations for estimating infiltration.

It is a spatially distributed watershed model capable of modeling at river basin scale. Potential evapo-traspiration is calculated using a method given by Priestley & Taylor (1972), soil evaporation and transpiration by Ritchie (1972) approach and the snowmelt by a simple degree-day equation (Knisel 1980). Surface runoff is calculated by a modified Soil Conservation Service curve number method given by King et al. (1999). The Cinematic storage model developed by Sloan et al. (1983) is utilized for interflow and lateral subsurface flows. The Muskingum method (Maidment 1993) gives the procedure of flow routing from one sub-basin to another.

It is a dynamic distributed model that depends on single storm events and operates for short time intervals of one minute to evaluate the physical description of hydrological processes in small catchments (Morgan et al. 1998). The erosion process is based on the KINEROS code with some minor additions (Woolhiser et al. 1990). Runoff estimation utilizes mass conservation and a simplified form of Saint-Venant equations while a dynamic mass balance equation of erosion is used for predicting soil loss.

It is an erosion model based on events and capable of simulating temporal fluctuations of sediment concentration and it utilizes the concept of the water balance model for overland flow estimation. The settling velocity characteristic defined by Lovell & Rose (1988) defines the influence of various factors on the rate of deposition.

It is a single storm, event based simulation model that analyses runoff by taking the results of kinematic wave equations (De Roo et al. 1996a, 1996b). Sediment yield analysis is carried out by generalized erosion and deposition mass balance equations. The parameters like infiltration and vertical movement can be analyzed through various methodologies such as:-

• SWATRE sub-model for all kinds of surfaces (Belmans et al. 1983).

• SWATRE sub-model for surface with wheel tracks (Belmans et al. 1983).

• SWATRE sub-model for crusted and non-crusted surfaces (Belmans et al. 1983).

• One layer Green & Ampt infiltration equation (Green & Ampt 1911).

• Two layer Green & Ampt infiltration equation (Green & Ampt 1911).

• Holtan infiltration equation (Holtan 1961; Overtone 1964).

• Assumption of zero infiltration for testing.

It is a physical simulation model capable of simulating hydrological parameters on an event or continuous steps using daily time steps (Laflen et al. 1997). WEPP is considered as one of the finest watershed models, capable of estimating overland flow and other hydrological parameters with high level of precision (Lane et al. 1997). A detailed description of the various analysis procedures performed by WEPP can be found in the model documentation given by Flanagan & Nearing (1995).

It is a physical model designed to simulate the hydrological parameters in all the basins including hills and areas with larger databases (Arnold et al. 1998; Setegn et al. 2008). SWAT can simulate the overland flow using two completely different methodologies. The SCS curve number method is the most used method but Green and Ampt. method is less data intensive (Fontaine et al. 2002).

These models are designed by U.S Army corps of Engineer's Hydrologic Center to simulate the hydrologic behavior and carry out the simulation processes by utilizing modified universal soil loss equation and SCS curve number methods for sediment and runoff respectively (Tahmasbinejad et al. 2012).

A huge number of watershed modeling tools have a capability to simulate the hydrological behavior realistically and with fair precision and can be applied to predict the effect of various factors on the hydrologic characteristics of catchment and can be used to address other environmental problems. However, all these models vary in analyzing the watershed parameters and need proper evaluation before applying any of these models for a particular purpose. In order to evaluate if the model has the ability to produce the desired results, 17 watershed models worldwide were reviewed and their results are summarized in Table 3. The performance of the model depends largely on the data utilized to support the model for the prediction process. Validating the results of the calibrated model is essential in determining the quality of the model, which requires comparison of its predicted values with the observed or measured values. Then the validated model can be used for predicting the hydrological parameters of other areas with similar characteristics. Furthermore, some cases have been added in Table 4 to compare different models to check the better model in the same set of conditions.

Each model has its own processing capabilities to analyze the hydrological behavior of a catchment. Their application depends on the aims and objectives as well as the degree of precision needed. On the basis of the review work, it is suggested that the following points should be considered before selection of any watershed model.

• Recognition of problem: The initial step in watershed modeling is to be well aware of the ideal results you require from the simulation process, so as to limit the danger of utilizing the wrong tools for any activity.

• Selection of models: Before model determination, one should realize what sort of system is to be modeled and what components need to be modeled. Furthermore, a researcher or a decision maker should be exact about the time steps for which the components are to be modeled, spatial and temporal variability and all other factors including the time available for collection of input data. Models that are capable of integration with geographic information systems and Arc softwares are preferred so as to save time to be expended in collection and preparation of input data and make the model more user-friendly. The models with GIS integration capacity are broadly to carry out the tasks of critical significance. The equations used by the model in processing the parameters should be thoroughly studied in order to analyze all the factors that could affect the desired outcome.

• Model assessment and sensitivity analysis: The factors that degrade or affect the quality and precision of the model must be identified and streamlined during the model assessment. Sensitivity analysis ensures that the model parameters are well designed to reproduce the desirable yield and determines the effect of these parameters on model execution. Simulation results can be compared to observed or measured values to ensure the model validity, even though these models need to carry sensitivity analysis and model calibration with field data before the validation process.

• Use of certified/acknowledged analyzing tool: The validated model can be used for predicting the hydrological behavior and impact of morphology on the other catchments after critical evaluation. However, uncertainties should be thoroughly quantified and evaluated before interpreting the results.

Floods and soil degradation have been recognized as a key problem for human sustainability with adverse economic and environmental impacts that make assessment of these terms of an utmost importance so that proper management practices can be applied to minimize the effect of these threats. The amount of runoff produced by any watershed needs to be appropriately predicted for planning and managing safety measures during the drought and flood conditions. Similarly, the amount of soil loss needs to be predicted and simulated to implement the best management practices to avoid silting of reservoirs and rivers.

This study gives a broad idea to a researcher or a decision maker for selection of an apposite and capable model for a given application. This paper reviewed a few mathematical models that can be used for simulating the hydrological parameters of different catchments across the world. A total of 17 models have been discussed in this study and each model varies in its applicability and analysis procedures. A comprehensive review of these models and their application worldwide revealed that SWAT, HEC-HMS, HEC-RAS, ANSWERS, WEPP and SHETRAN models are the most capable ones for prediction and assessment of various hydrological parameters like runoff, soil yield and nutrient losses, and hence these physical watershed models are more reliable for accomplishing sustainable watershed management practices. However, there are many more models that give good results and can be run for different conditions in different watersheds. Furthermore, a model can perform well in one range of conditions and lack its performance in another set of conditions; therefore, it becomes necessary to choose the appropriate model for the particular watershed after proper evaluation to get the accurate and desired results.

Watershed models reviewed in this paper are summarized in Tables 1 and 3. Research scholars and decision makers can use this study as a screening tool for selecting a watershed model for a given application. Furthermore, researchers can choose the best model suitable for different areas like small agricultural catchments, rangelands, large basins and hilly areas etc. A decision maker can select the model based on the requirements of a study, like analysis of gully erosion, sub-surface flow or flow through unsaturated zone.

The author confirms that the data supporting the findings of this study are available within the article.