Identification and Prioritisation of Critical Sub-watersheds for Soil Conservation Management using the SWAT Model

Abstract

A few areas of the watershed are critical and responsible for high amount of soil and nutrient losses. Implementation of best management practices is required in those critical erosion prone areas of the watershed for controlling the soil and nutrient losses. Identification of these critical areas is essential for the effective and efficient implementation of watershed management programmes. In this study, a calibrated Soil and Water Assessment Tool (SWAT) model was verified for a small watershed (Nagwan) and used for identification and prioritisation of critical sub-watersheds to develop an effective management plan. Daily rainfall, runoff and sediment yield data of 7 years (1992–1998) were used in this study. Data related to nutrient losses for few storm events of 1997 were also used. Besides these data, the topographical map, soil map, land resources data and satellite imageries of the study watershed were used in this study.

A geographical information system was used for generating the watershed and sub-watershed boundaries, drainage networks, slope, soil series and texture maps. Supervised classification method was used for land use/cover classification from satellite imageries. The weighted average values of parameters such as runoff curve number, surface slope, channel length, average slope length, channel width, channel depth, soil erodibility factor and other soil layer data were taken for each sub-watershed to verify the model.

The calibrated SWAT model was verified for the monsoon season on daily basis for the year 1997 and monthly basis for the years 1992–1998 for both surface runoff and sediment yield. It was also tested for the available data on nutrient losses. Critical sub-watersheds were identified on the basis of average annual sediment yield and nutrient losses during the period of 3 years 1996–1998. The erosion rates and their classes were used as a criterion for identifying the critical sub-watersheds. Out of the 12 sub-watersheds, one sub-watershed fell under moderate soil loss group and five sub-watersheds fell under high soil loss group of soil erosion classes whereas other sub-watersheds fell under slight erosion classes. The study revealed that the SWAT model could successfully be used for identifying and prioritising critical sub-watersheds for management purposes.

Introduction

The Resource considerations for implementation of watershed management programmes or various other reasons related to administration or even political considerations may limit the implementation of management programmes to a few sub-watersheds only. Even otherwise, it is always better to start management measures from the most critical sub-watershed, which makes it mandatory to prioritise the sub-watershed available. Watershed prioritisation is thus the ranking of different critical sub-watersheds of a watershed according to the order in which they have to be taken up for treatment and soil conservation measures

The intensive study of individual watersheds is necessary to enable management plans to be developed and also to apply the results of one watershed, to another with similar characteristics. Effective control of soil and nutrient losses requires implementation of best management practices in critical erosion prone areas of the watershed. It can be enhanced by the use of physically based distributed parameter models, remote sensing technique and geographic information system that can assist management agencies in both identifying most vulnerable erosion prone areas and selecting appropriate management practices.

Numerous studies have indicated that, for many watersheds, a few critical areas are responsible for a disproportionate amount of the pollution (Dickinson et al., 1990; Dillaha, 1990; Maas et al., 1985; Storm et al., 1988). Critical areas of non-point source pollution can be defined both from the land resources and the water quality perspectives (Maas et al., 1985). From the land resource perspective, critical areas are those land areas where the soil erosion rate exceeds the soil loss tolerance value. Critical areas from the water quality perspectives are areas where the greatest improvement can be achieved with the least capital investment in best management practices.

The average soil loss value of 16·4 t ha⁻¹ yr⁻¹ (Dhruva Narayana, 1993) and permissible soil loss value of 11·2 t ha⁻¹ yr⁻¹ (Mannering, 1981) can be taken into consideration for identifying the critical sub-watershed. Priorities can be fixed on the basis of ranks assigned to each critical sub-watershed according to ranges of soil erosion classes described by Singh et al. (1992) for the Indian condition. They categorised the soil loss ranges into different soil erosion classes. Soil erosion classes such as slight (0–5 t ha⁻¹ yr⁻¹), moderate (5–10 t ha⁻¹ yr⁻¹), high (10–20 t ha⁻¹ yr⁻¹), very high (20–40 t ha⁻¹ yr⁻¹), severe (40–80 t ha⁻¹ yr⁻¹) and very severe (>80 t ha⁻¹ yr⁻¹) were reported by Singh et al. (1992).

An average soil loss tolerance value of 9·0 t ha⁻¹ yr⁻¹ was used for Nomini Creek watershed located in Westmoreland County, Virginia by Tim et al. (1992) in their study for identifying the critical areas from the land source prospective. They also considered a threshold value for the loading rate P of 1·12 kg ha⁻¹ yr⁻¹. This threshold value of P loading was obtained from the work of DelRegno and Atkinson (1988). Tim et al. (1992) classified watershed areas into three classes i.e. low, medium and high potential areas from both land resource and water quality prospective. Also for nutrient losses a threshold value of 10 mg⁻¹l for nitrate nitrogen and 0.5 mg ⁻¹l for dissolve phosphorous as described by EPA (1976) can be considered as criterion for identifying the critical sub-watersheds.

The Soil Conservation Department of Damodar Valley Corporation (DVC) Hazaribagh, Bihar (India) has demarcated 20 prioritised sub-watersheds out of 39 sub-watersheds for treating them with the appropriate soil conservation measures (Misra, 1986). The prioritisation of these sub-watersheds was based on an empirical formula developed by DVC using a limited stream flow record of only 3 years. They considered three priority criteria, i.e. priority I (erosion index of 30 and above), priority II (erosion index lying between 15 and 30) and priority III (erosion index less than 15). The actual formula used for calculating the erosion index I_E is as follows:

$$\text{I}{}_{\mathrm{E}}\text{=}\text{A}{}_{\mathrm{u}}\text{3}\text{+A}{}_{\mathrm{gw}}\text{+A}{}_{\mathrm{df}}\text{+}\text{A}{}_{\mathrm{f}}\text{5}$$

where: A_u is the upland area in ha; A_gw is the gullied wasteland area in ha; A_df is the denuded forestland area in ha; and A_f is the wood forestland area in ha.

Several techniques, ranging from manual overlay of spatially -index mapped data to pollutant yield modelling, have been used to characterise and delineate critical areas of non-point source pollution in complex landscapes. McHarg (1969) used a manual map overlay system to display the common attributes of selected land areas in order to make decisions on the type and degree of land development that is commensurate with the physical properties and limits of an area. Recently, there has been a shift towards the use of computerised data management systems to facilitate the delineation of critical areas of non-point source pollution (Hession & Shanholtz, 1988; Vieux, 1991). Some of the research workers used the sediment yield index I_sy method for prioritisation of sub-watersheds (Karale et al., 1975, 1977). They used the following equation for computation of I_sy.

$$\text{I}{}_{\mathrm{sy}}\text{=}\text{∑}\text{E}{}_{\mathrm{i}}\text{A}{}_{\mathrm{ei}}\text{D}{}_{\mathrm{r}}\text{A}{}_{\mathrm{w}}\text{100}$$

where: I_sy is the sediment yield index; E_i is the weighing value of erosion intensity mapping unit; A_ei is the area of the erosion intensity mapping unit in a watershed in ha; D_r is the delivery ratio; and A_w is the total area of watershed in ha.

Several physically based distributed parameter models (ANSWERS, AGNPS, SHE, SWRRB and SWAT) have been developed to predict runoff, erosion, sediment and nutrient transport from agricultural watersheds under various management regimes. Among these models, Soil and Water Assessment Tool (SWAT) is the most recent one used successfully for simulating runoff, sediment yield and water quality of small watersheds. The SWAT model is a distributed parameter, continuous model developed by the USDA-ARS (Arnold et al., 1996, 1998). The SWAT model was tested mainly on monthly and annual basis for predicting runoff and sediment yield (Srinivasan et al., 1993; Srinivasan & Arnold, 1994; Rosenthal et al., 1995; Bingner, 1996; Bingner et al., 1997; Peterson & Hamlett, 1998). However, Tripathi et al. (1999a, 1999b) tested the SWAT model on the basis of daily runoff and sediment yield for an Indian watershed, namely Nagwan in the Hazaribagh district of the Bihar State in India. Limited research work on identification of critical sub-watersheds and assessment of the impact of management practices on runoff, sediment yield and nutrient losses using SWAT has been reported (Arnold et al. 1999; Santhi et al., 2001). Also the testing of SWAT model on daily basis for the nutrient losses has not appeared much in the literature.

Keeping the above facts in mind, the current study was undertaken with the use of a verified model i.e. SWAT to identify the critical sub-watersheds on the basis of estimated sediment yield and nutrient losses of a small watershed for the purpose of developing the effective management plan.