Environ Earth Sci DOI 10.1007/s12665-013-2439-3
ORIGINAL ARTICLE
Geospatial assessment of soil erosion vulnerability at watershed level in some sections of the Upper Subarnarekha river basin, Jharkhand, India Shuvabrata Chatterjee • A. P. Krishna A. P. Sharma
•
Received: 12 January 2012 / Accepted: 21 March 2013 Ó Springer-Verlag Berlin Heidelberg 2013
Abstract Undulating landscapes of Chhotanagpur plateau of the Indian state of Jharkhand suffer from soil erosion vulnerability of varying degrees. An investigation was undertaken in some sections of the Upper Subarnarekha River Basin falling within this state. An empirical equation known as Universal Soil Loss Equation (USLE) was utilized for estimating the soil loss. Analysis of remote sensing satellite data, digital elevation model (DEM) and geographical information system (GIS)–based geospatial approach together with USLE led to the soil erosion assessment. Erosion vulnerability assessment was performed by analyzing raster grids of topography acquired from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM data. LANDSAT TM and ETM? satellite data of March 2001 and March 2011 were used for inferring the land use–land cover characteristics of the watershed for these years, respectively. USLE equation was computed within the GIS framework to derive annual soil erosion rates and also the areas with varying degrees of erosion vulnerability. Erosion vulnerability units thus identified covered five severity classes of erosion ranging from very low (0–5 ton ha-1 yr-1) to very severe ([ 40 ton ha-1 yr-1). Results indicated an overall increase of erosion in the year 2011 as compared to the erosion computed for the year 2001. Maximum soil erosion rate during the year 2001 was found up to 40 ton ha-1 yr-1, whereas this went up to 49.80 ton S. Chatterjee (&) A. P. Sharma CIFRI, Indian Council of Agricultural Research (ICAR), Kolkata, India e-mail:
[email protected] A. P. Krishna Department of Remote Sensing, Birla Institute of Technology (BIT), Mesra, Ranchi, India
ha-1 yr-1 for the year 2011. Factors for the increase in overall erosion could be variation in rainfall, decrease in vegetation or protective land covers and most important but not limited to the increase in built-up or impervious areas as well. Keywords Watershed management Soil erosion USLE Remote sensing LULC change Subarnarekha River Basin
Introduction Watersheds are considered the basic units of resources and watershed management aims to rationalize the land and water resource use for optimal production with least adverse impacts to the natural resources (Sharma et al. 1998). Watershed management concept recognizes the linkages between uplands, low lands, land use, geomorphology, slopes and soils (Mishra and Nagarajan 2010). In India, soil and water conservation are key issues behind demarcating the priority watersheds (Khan et al. 2001). A variety of landscapes exist with varying vulnerability to erosion in the large watersheds like Subarnarekha within the Chhotanagpur plateau area of Jharkhand state in India (Krishna and Hemrom 2008). This river basin is highly dissected by the rivers and streams of varying dimensions. Erosion is a natural geomorphic process that was active during the whole geological time and formed the earth’s surface (Bathrellos and Skilodimou 2007). Soil is naturally removed by the action of water or wind and such occurrences of soil erosion have been taking place over the geological past. Soil erosion is an important characteristic signifying the natural physical processes, which is in effect within the watersheds. This is a gradual process that occurs when the
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impact of water detaches and removes soil particles, causing the soil to deteriorate (Ni et al. 2004; cf. Shinde et al. 2011). It has long-term effects on the quality of cultivable soil and the agricultural productivity, quality of water, transport of sediments, the changes in river channel and impacts on flooding (Morgan 1995). Land use and land cover (LULC) changes may also lead to significant impacts on the hydrological cycle and water quality even causing floods, droughts and changes in the river and groundwater regimes. Land use/land cover studies and application of tools and techniques in estimating the change, and assessment of impacts on soil erosion have evoked interests among many researchers. Rapid growth of population has also brought about extensive LULC changes in many Himalayan watersheds leading to reduced ground water recharge, increased runoff and thereby, increased soil erosion (Tiwari 2000). Potential effects of LULC changes on soil erosion at different spatio-temporal scales have been investigated in different geographical locations of the world. These include studies at the scales of small watersheds (Favis-Mortlock and Boardman 1995; Pruski and Nearing 2002; Dunjo et al. 2004; Van Rompaey et al. 2005; Jordan et al. 2006; Nearing et al. 2005; Cebecauer and Hofierka 2008) as well as regional scales (Yang et al. 2003). LULC change dynamics of the watersheds are significant for understanding the pressures on its resources (Krishna 1996a). Increasing human population, deforestation, agricultural cultivation and inability of farmers to adopt optimal soil conservation measures within the catchments seem to magnify the problem of erosion. Similarly, effects of LULC have been studied at temporal scales of a few years (Neil Munro et al. 2008; Siyuan et al. 2007) to a number of decades (Martha et al. 2008; Szilassi et al. 2008; Piccarreta et al. 2006). Soil erosion has been identified as one of the problems of both rural and urban landscapes all over the world. Developing as well as developed countries face problems of soil erosion of varying intensity and nature. In a country like Nigeria, the problem of soil erosion is exacerbated by the increased rainfall and flood events coupled with the increasing rate of unplanned urbanization (Jeje 1988, 2005; Ibitoye and Eludoyin 2010). In China, many land-related problems have been identified, including agricultural land loss, water pollution, soil erosion and an increase in the magnitude and frequency of flooding in recent years (Yeh and Li 1999). Over the past few decades, vital resources of almost all the watersheds of Iran have been subjected to rapid deterioration resulting from expanding anthropogenic activities (Mahmoudi et al. 2010). The potential for surface runoff and soil erosion is greatly affected by the land use (Van Rompaey et al. 2002). Soil erosion caused by the change in land use as well as rapid urbanization has
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numerous detrimental on-site impacts on the arable land, including the loss of topsoil and fertilizers, decreased crop yield in the short term and decreased soil productivity in the long term (Ward 2009). In the areas where climate, soil and topography are similar, soil erosion rates are commonly related to the land use/land cover (Del Mar et al. 1998). Contribution of land use change to the soil erosion particularly due to urbanization has been brought out in the study of Kali basin of Lake Balaton, Hungary by Szilassi et al. (2008). Similarly, the impact of land use change on the soil erosion due to excessive urban runoff, artificial drainage and increased flood volumes and peaks are also well reported from different basins of the world (Fernandez 2009; Hammer 1972; Hollis and Luckett 1976; Neller 1988).There had been increase in the runoff and soil erosion due to increase of impervious cover owing to the population boom upon relocation of Dell Computer Corporation headquarters to Round Rock Texas in 1994 (Barnett and Franco 2004). In the Muskegon River Watershed of Lake Michigan, the increase in impervious surface led to the altered natural hydrologic conditions. The outcome of the alteration was typically reflected in the increases in the volume and rate of surface runoff and the decreases in ground water recharge and base flow (Carter 1961; Andersen 1970; Lazaro 1990; Moscrip and Montgomery 1997). Such alterations eventually led to larger and more frequent incidents of the local flooding (Field et al. 1982; Hall 1984), reduced residential as well as municipal water supplies and decreased base flow into stream channels during dry weather (Harbor 1994). Shape of landscape and surface roughness are key properties for understanding the factors related to the management of water and natural resources. These properties need to be observed, measured and evaluated quantitatively at large scales to understand the relationship of hydrologic systems with natural and agricultural landscapes. It is difficult and time-consuming to measure surface roughness at scales which are large enough for understanding water movement on the landscape using traditional techniques (Ritchie et al. 1997). Geospatial techniques viz. remote sensing and GIS provide a quicker and cost-effective analysis for relevant applications with accuracy for planning (Jabbar 2003; Krishna 1996a, b; Krishna and Rai 1996). A number of parametric models have been developed to assess soil erosion vulnerability of drainage basins. Universal Soil Loss Equation (USLE) is a largely used empirical method for quantifying soil erosion taking into account various contributing factors (Wischmeier and Smith 1978). For watershed-based computation of soil erosion, remote sensing and GIS are widely used, especially employing USLE method (Hemrom 2007; Maria Soupios and Vallianatos 2009; Chen Tao et al. 2010; Bez 2011). Qualitative and quantitative models provide appropriate information about the spatial distribution of
Environ Earth Sci
erosion-risk areas in the watershed where suitable and urgent measures and treatments will be required (Kefi et al. 2011). USLE predicts soil loss for a given site as a product of six major erosion factors whose values at a particular location can be expressed numerically and is suitable for predicting long-term averages. Spatial patterns of soil erosion play an important role in studying sources of erosion, sinks as well as soil and water conservation (Shinde et al. 2011). Prediction of soil loss is important for assessing soil erosion hazard, determining suitable land use and soil conservation measures for a catchment (Baskan et al. 2010). Several studies have been conducted in India (Narain et al. 1994; Ali and Sharda 2005; Sharda and Ali 2008) and the soil erosion severity ranges were established based on the rankings of mean annual soil erosion rates. Land degradation from water-induced soil erosion is becoming a serious problem in the Jharkhand state of India, and there is less information available on factors affecting soil erosion in the study area. Therefore, this work has attempted to establish the erosion vulnerability units (EVUs) with a view to contribute to proper management and planning of this watershed falling within upper Subarnarekha River Basin.
Study area Subarnarekha River sub-basin occupies an approximate area of 12,811 km2 before draining into the neighboring states of Orissa and West Bengal. Following the nomenclature of Central Ground Water Board, India, watershed SRBH006, covering an approximate area of 300 km2, has been investigated. Geographically, this watershed is situated within the latitudes 23°140 N to 23°250 N and longitudes 85°100 E to 85°270 E. One of the significant aspects of this watershed is the location of Ranchi city within it, which is capital of the Indian state of Jharkhand created in the year 2000. Therefore, this watershed may serve as an indicator of increasing pressures on the resources in keeping pace with the relatively rapid development activities after creation of this state. Significant portions of some revenue blocks of Ranchi district are situated within the watershed (Fig. 1). These are Kanke, Ratu, Namkum and a small portion of Angara block representing a large variety of natural and anthropogenic impact conditions contributing to the erosion vulnerability. Origin of the Subarnarekha River lies within this watershed and many tributaries branch out to the north as well as south directions from this eastwardly flowing river. This basin experiences subtropical climate with well-distributed rainfall during southwest monsoon from June to October months. Average annual rainfall varies between 1,100 and 1,400 mm with maximum rainfall experienced
during June to October months, with about 90 % of total annual rainfall being received during the monsoon season. Winter season is marked by dry and cold weather during the months of November to February. Geologically, this sub-basin has alluvial soils, boulder conglomerates, older alluvium and laterites in the central portion; basic and ultrabasic rocks in the southern part and the remaining portion composed of Chhotanagpur granite–gneiss and granophyres. Major landforms within this watershed are buried pediments, detritus, pediplain, valley fills, denudational hills, structural ridges and laterite capping, etc. The altitude of the watershed ranges between 590 and 760 m above mean sea level (amsl) with northernmost portion covered by ridges and hillocks of altitudes of 700 m amsl or more.
Materials and methods There are several classical empirical methods for estimation of soil erosion vulnerability which includes USLE or the Universal Soil Loss Equation (Wischmeier 1959). USLE model in conjunction with remote sensing (RS) and geographical information system (GIS) proves very useful to estimate the soil erosion. This study utilized the above geospatial tools together with digital remote sensing data such as digital elevation model (DEM) and other earth observation (EO) satellite data. Such combination of tools and techniques are largely used worldwide by soil erosion researches (Vrieling 2006, 2007). Detailed analyses of factors of soil erosion related to topographic indices were computed using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (GDEM) data with 30 m resolution produced by the Ministry of Economy, Trade and Industry of Japan (METI) and the National Aeronautics and Space Administration (NASA). Creation of database through conventional methods is usually time-consuming, tedious and complex to handle. In this study, remote sensing data were utilized to generate information essential for the analysis of USLE factors. For this purpose, LANDSAT satellite data of March 2001 and March 2011 were used, which were obtained from the Global Land Cover Facility (GLCF) of United State Geological Survey (USGS) (http://glcf. umiacs.umd.edu/data/landsat/). Out of the eight spectral bands of LANDSAT Enhanced Thematic Mapper (ETM?), bands 1 (blue), 2 (green), 3 (red) and 4 (near infrared) were used. Bands 4, 3 and 2 were used for generating the standard false color composite (FCC) images applying suitable color filters. On these FCCs, vegetation appeared in shades of red, urban areas in cyan to blue colors and color of soil varied from dark to light brown. Such FCCs are widely being used for
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Fig. 1 Map of the study area of the watershed in Upper Subarnarekha River Basin, Jharkhand state, India
vegetation studies, monitoring of various stages of crop growth, soil and drainage patterns. Deep red hues in general indicated broad leaf and/or healthier vegetation while lighter shades of red signified grasslands or sparsely vegetated areas including those under crop lands with standing crops. Whereas densely populated built-up urban areas were observed in cyan to light blue colors. This TM band combination usually gives results similar to the traditional color infrared aerial photography (Lillesand and Kiefer 1999). LANDSAT ETM?/TM data for all the bands used in this study had spatial resolution of 30 m. LANDSAT ETM? satellite data for the study area of March 2001 (Fig. 2a) and March 2011 (Fig. 2b) were used. Image of March 2011 obtained from GLCF suffered from scan line corrector (SLC) failure leading to SLC-off mode data. Scan line corrector (SLC) failure of ETM? sensor took place onboard on May 31, 2003 causing the scanning pattern to exhibit wedge-shaped scan-to-scan gaps. ETM? continued to acquire data with the SLC powered off and acquiring the images missing approximately 22 percent of the normal scene area. Since locations of the scan gaps are different
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for each SLC-off scene, the gap-filling process was possible to undertake which accounted for the scan gap interactions. Based on extensive literature survey and the methods thus gathered by the works of Storey et al. (2005), Maxwell et al. (2007), Zhang et al. (2007) and Pringle et al. (2009), etc., this ETM? image of March 2011was rectified using suitable gap fill techniques (Fig. 2c).
Universal Soil Loss Equation (USLE)–based computations Since this study aimed to understand an important hydrological aspect of the watershed in terms of soil erosion vulnerability, EVUs were computed across the entire watershed. For this, USLE developed by the United States Department of Agriculture (USDA), Agricultural Research Service (Wischmeier and Smith 1978) was used (Eq. 1). Although it is an empirical model, the combined use of remote sensing, GIS and USLE techniques makes soil erosion estimation and its spatial distribution feasible
Environ Earth Sci
Fig. 2 LANDSAT ETM? satellite image of a March 2001, b March 2011 (SLC-off), c March 2011 (after SLC-off correction)
within reasonable costs and better accuracy even for larger areas (Millward and Mersey 1999; Lin et al. 2002; Wang et al. 2003; Lu et al. 2004; Jasrotia and Singh 2006; Krishna Bahadur 2009; Chou 2010). This method of soil erosion assessment takes into account several factors such as climatological (rainfall erosivity), pedological (soil erodability), topographic (slope length and steepness) and anthropogenic (cover management and supporting conservation practices) approaches which are further supported by the land cover data (Shinde et al. 2011). Annual soil loss per unit area (A) was obtained using the aforementioned USLE (Wischmeier and Smith 1978) equation (Eq. 1): A ¼ R K LS C P where, A is the mean annual soil loss (ton ha-1 yr-1), R is the rainfall and runoff erosivity (MJ ha-1 mm-1 yr-1),
ð1Þ
factor
K is the soil erodibility factor (ton MJ-1 mm-1), LS is the topographic factor comprising slope length factor (L) and slope steepness factor (S), C is the cover management factor, and P is the conservation support-practices factor Rainfall and runoff erosivity factor (R) R is the long-term annual average of the product of events of rainfall kinetic energy in MJ ha-1. For the study area, coordinate-based rainfall data were unavailable for multiple points rather there was availability of data for certain recording stations of representative locations of the watershed. These locations were within the similar climatic zone under Kanke block encompassing maximum area to the extent of 55 % of the watershed. Its entire area falls almost in a plateau region and there is not much variation either in physiology or climate within its water divides. All these recording stations had similar recorded rainfall data
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range; therefore, data from one of the centrally located station was considered to represent the most representative rainfall record. Monthly rainfall data (Fig. 3a) of this station within Kanke block were acquired and used for the preparation of R factor raster grid for the years of 2001 and 2011. Rainfall erosivity was calculated using the equation (Eq. 2) developed by El-Swaify et al. (1983) R ¼ 38:5 þ 0:35ðrÞ
ð2Þ
where, r is the total annual rainfall in mm. The available rainfall information was extrapolated within the study area in terms of spatial distribution using Arc GIS software. Based on extensive literature survey, the methodology to calculate R factor was ascertained in conformity with the climatic conditions of the area. Eiumnoh (2000) used the above equation to obtain acceptable R factor for tropical and subtropical ecological zones. Using this equation, appropriate results were also found by Merritt (2002), Hartcher et al. (2005), Krishna Bahadur (2009) and Pal et al. (2012). They tested and applied this equation for subtropical climatic areas with almost similar rainfall pattern as the watershed being investigated. Despite being empirical in nature, this equation (Eq. 2) was considered suitable for Fig. 3 a Monthly rainfall for the years 2001 and 2011 in the study area, b Subtropical rainfall pattern (Source http://www.climate-charts.com)
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the calculation of R factor for this watershed as well. Rainfall pattern of this study area, which falls within the subtropical climatic zone, is shown in Fig. 3b. R factor for the years 2001 and 2011 was calculated and found to be 477.12 MJ ha-1 mm-1 yr-1 and 534.10 MJ ha-1 mm-1 yr-1, respectively. Soil erodibility factor (K) Soil erodibility (K) represents the susceptibility of soil or surface material to erosion, transportability of the sediment and the amount and rate of runoff given a particular rainfall input, as measured under a standard condition. The standard condition is the unit plot of 22.2 m in length with a 9 percent gradient, maintained in continuous fallow and tilled up and down the hill slope (Weesies 1998). Since the soil found in this watershed is of Alfisol type, K factor was calculated using the K factor Nomograph of Foster et al. (1981). Soil map of the study area was prepared using data from the available reports on soil survey of the Ranchi district prepared by National Bureau of Soil Survey and Land Use Planning (NBSS & LUP), Regional Centre, Kolkata, and that from the Department of Soil Science & Agricultural Chemistry, Birsa Agriculture University
Environ Earth Sci
(BAU), Ranchi, Jharkhand. Different soil classes were then plotted on the Nomograph. The Nomograph has been supported by the soil structure code table (Table 1, after Schwab et al. 1992) and Soil Permeability Code Table (Table 2, after Schwab et al. 1992). Parameters upon which the Nomograph was prepared are shown in Table 3 and the corresponding spatial details are shown in Fig. 4. Topographic factor (LS) Out of numerous topographic indices which can be extracted from DEM, only severe erosion-related indices were considered for analysis. These include slope, LS factor, aspects, terrain relief, etc. Basically, the LS factor can be estimated through field measurements or from a DEM. With the incorporation of DEM into a GIS, the slope gradient (S) and slope length (L) were determined accurately and combined to form a single factor known as the topographic factor LS. The precision with which it can be Table 1 Soil structure code (after Schwab et al. 1992) Code
Structure
Size (mm)
1.
Very fine granular
\1
2.
Fine granular
1–2
3.
Medium-to-coarse granular
2–10
4.
Blocky, platy or massive
[10
Table 2 Soil permeability code (after Schwab et al. 1992) Code
Description
Rate (mm/h)
1.
Rapid
[130
2.
Moderate to rapid
60–130
3.
Moderate
20–60
4.
Slow to Moderate
5–20
5.
Slow
1–5
6.
Very slow
\1
estimated depends on the resolution of the DEM. Slope in upper Subarnarekha Basin was obtained from preprocessed sink–filled ASTER Global DEM. Analysis gave rise to the varying slope angles of this watershed ranging up to 84 %. Significant slope-related parameter considered is slope length (LS) factor, which combines the effects of slope length L and slope gradient S. Generally, as hill slope length and/or hill slope gradient increase, soil loss increases. L*S factor was calculated using this equation in spatial analyst of Raster calculator in Arc GIS software with the help of modified formula in a raster form. pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi LS ¼ L=22ð0:065 þ 0:045 S þ 0:0065Þ S2 where L is the slope length (m), S is the slope steepness (radians). Pixel-wise LS factor map was created from the DEM by calculating a slope (grid radian). For better accuracy, the same has been tried with ARC Macro Language (AML) and the value which was calculated ranges from 0 to 22. With a smooth average slope, sediment transport is reduced on a warped or concave slope (due to localized sedimentation), but increased on a convex slope due to the gradient of the steepest portion (Wischmeier and Smith 1978). The presence of concave slopes in a landscape indicates that there must be trapping, siltation and colluvial deposit in the valley. Therefore, factors such as aspect distribution, slope distribution and elevation frequency maps were derived using ASTER GDEM for the study area (Fig. 5). These aspects were observed with limited field checks indicating that the topographic factors such as slope, slope length and the derived LS had larger impacts on the soil erosion rather than the convexity or concavity of the surface in the study area. Cover management factor (C) Soil loss is very sensitive to vegetation cover along with slope steepness and length factor (Benkobi et al. 1994;
Table 3 Parameters used for the calculation of K factor S. no.
Soil class no. (as in Fig. 4)
Soil type
Silt ? very fine sand
% of sand
OM
Structure code
Permeability code
K Factor
1.
33
Fine, mixed, hyperthermic Typic Paleustalfs with Fine, mixed, hyperthermic Typic Rhodustalfs
70
20
1.075
1
5
0.066
2.
34
Fine loamy, mixed, hyperthermic Typic Paleustalfs & With Fine loamy, mixed, hyperthermic Typic Rhodustalfs
65
30
1.307
1
4
0.062
3.
36
Fine, mixed, hyperthermic Typic Paleustalfs & Fine loamy, mixed hyperthermic Typic Rhodustalfs
30
70
1.307
2
3
0.052
4.
39
25
50
1.307
2
2
0.068
5.
40
Fine, mixed, hyperthermic Rhodic Paleustalfs & Fine loamy, mixed, hyperthermic Typic Haplustepts Fine loamy, mixed, hyperthermic Typic Haplustepts & Fine loamy, mixed, hyperthermic Typic Haplustalfs
20
75
1.075
3
1
0.064
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Fig. 4 Nomographs used for calculating K factor
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Environ Earth Sci Fig. 5 a Sink-filled ASTER GDEM, b Aspect distribution map, c Cumulative slope distribution map, d Elevation versus Slope map, e Elevation frequency map
Fig. 6 Land use land cover map a year 2001 and b year 2011
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Environ Earth Sci Table 4 Cover management factor (C) values for different LULC classes of the study area S. No.
LULC class
C value
1.
Built up
0.000
2.
Agricultural land
0.400
3.
Dense vegetation
0.004
4.
Sparse vegetation
0.030
5.
Barren land
1.000
6.
Water body
0.000
factor was assigned for different land use classes as shown in Table 4. Conservation support-practices factor (P)
Biesemans et al. 2000). Vegetation cover protects the soil by dissipating the raindrop energy before reaching soil surface. The value of C depends on vegetation type, stage of growth and cover percentage (Gitas et al. 2009). Higher values of C factor indicate no cover effect and soil loss comparable to that from a tilled bare fallow, while lower C means a very strong cover effect resulting in no erosion (Erencin, 2000). In this study, land use/land cover map derived from satellite images (Fig. 6) served as a guiding tool in the allocation of C factors for different land use classes. The C factor values were the representative values for allocating the USLE land cover and management factors corresponding to each crop/vegetation condition. Land use classes of the watershed were identified as (1) built-up land (2) agricultural land (3) dense vegetation (4) sparse vegetation (5) barren land and (6) water bodies. Based on USDA-SCS (1972) and Rao (1981), the cover management
Fig. 7 P factor Map for the years 2001 and 2011
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Support-practices factor for this watershed was verified with field-level investigations. In this area, less tillage practices were noticed along with few tea/coffee plantations in certain villages of southeastern portion of the watershed within Namkum block. Therefore, these were not taken into account due to their very less spatial extent. Support-practices factor P represents the effects of those practices that help prevent soil from eroding by reducing the rate of water runoff. The values of P are calculated as rates of soil loss caused by a specific support practice divided by the soil loss caused by row farming up and down the slope. There are no support practices in place within the study site at present. In the absence of support practice such as contour tillage, contour strip cropping and terraces, the common practice is to assign a value of 1 for the P factor. After calculating the estimated soil loss by USLE, the P factor values can be adjusted to forecast various erosion prevention measures. A new approach was also tested by assigning higher values for the impervious areas, mostly covered by the built-up area land cover along with major road networks. According to Goldman et al. (1986), construction sites which are compacted and solidified never allow water to percolate below it, rather helped
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it to flow with greater speed. Therefore, they assigned a higher value of 1.3 for conservation practice factor over smooth and impervious areas. Based on such considerations, the built-up areas were clipped out and assigned similar values as shown in Fig. 7. For understanding the nature and extent of change in the built-up area, data from band 7 of TM for the year 2001 and that of ETM? for the year 2011, respectively, were analyzed using ERDAS Imagine software. This helped to understand the overall change scenario over a period of 10 years. Change in the built-up area was observed, as an increase in this category could be verified by the band differencing and band ratioing process. Change detection method was used to understand the differences in LULC, mainly in urban areas. For this, an image difference map was generated from band 7 and the ‘highlight change’ was calculated. Calculation of highlight change file was done at 15 % which implies an increase of more than 15 % and a decrease of more than 15 %. Model builder of ERDAS
Imagine was used to produce a different image by band differencing. The difference was very explicit (Fig. 8) and it could be used for the validation of LULC classification too. Dark and bright shades reflect the decreased and increased values with better precision. Water bodies were also considered with high values due to the fact that there was a difference, but these water bodies were ignored ultimately.
Result and discussions Annual soil erosion rate of the watershed was ascertained for the years 2001 and 2011, respectively, with the help of USLE together with the geospatial techniques. An extensive increase in the built-up area has been observed for the year 2011 when compared to that of 2001. Although rainfall erosivity factor increased relatively between the years 2001 and 2011, increase in the built-up area has also
Fig. 8 a Band-differenced image of Landsat TM band 7 of March 2001 and Landsat ETM? band 7 of March 2011, b Highlight change image (±15 %), c Band differencing Model created in ERDAS Imagine, d Model-induced band-differenced map
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Environ Earth Sci Fig. 9 a Change in LULC for the years 2001 and 2011, b Change in area under different soil types, built up and water bodies
been a cause of concern with respect to soil erosion in the watershed. This has resulted in an increase in the area of land under impervious surfaces leading us to understand that there would be less area prone to erosion. But actual conditions can be understood in terms of increased scouring of the soil largely due to the associated kinetic energy of the rain drops leading to more erosion but over relatively less area of pervious nature exposed in the year 2011. Increased runoff due to increased impervious area may have intensified the process further. Urban development facilitates sedimentation and contaminant transport directly (Cablk and Perlow 2006). This is particularly so in view of the paved surfaces being impervious that does not allow percolation into the underlying soil or substrate. As a result, water accumulates and can move with greater velocities over these impenetrable surfaces until it moves onto an unpaved surface. Since impervious surfaces concentrate and accelerate the accumulation and flow of water, the water’s force leaving the paved surfaces is capable of eroding or moving surface soil from the unpaved surface that would otherwise not erode to that extent. Contaminant transport into water bodies occurs through the same physical process but involves contaminants from various sources that accumulate on paved surfaces. In fact, it has been shown that impervious surfaces are a major contributor to watershed degradation and can be used as indicators of watershed integrity (Arnold and Gibbons 1996). Overlaying the land use land cover (LULC) maps of the years 2001 and 2011 revealed that increase in the areas under urban or built-up land were at the expense of other types of land covers such as agriculture and
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vegetation, etc. These maps were further overlaid onto the soil map revealing the areal extent and spatial expansion of built-up land cover within various soil categories. Area under the built-up land cover increased from 17.9 % in 2001 to 37.14 % in 2011 (Fig. 9a). Barren land which occupied an area of 5.05 % in 2001 increased to occupy an area of 6.45 % in 2011. Share of agricultural land in the total land use decreased from 42.07 % in 2001 to 36.17 % in 2011. Sparse vegetation too decreased from 15.14 % in 2001 to 7.13 % in 2011. Similarly, dense vegetation decreased from 17.68 to 11.9 % and water body from 2.16 to 1.21 %, respectively, over the years 2001 to 2011. Overall decrease in the spatial extent of natural vegetation cover can be attributed to the relative increase in barren and built-up land. Considering increases in the built-up as well as barren land, spatial changes within various soil types were assessed. LULC maps of 2001 and 2011, respectively, were overlaid over the soil map of NBSS & LUP. This helped in the assessment of soil types and to account for changes in their spatial extent particularly with reference to the increase in builtup land implying conversion of greater areas into the impervious surfaces. Figure 9b shows that the areas covered by silty clay loam changed from 180 km2 in 2001 to 140 km2 in 2011. Silty clay occupied an area of 3.09 km2 in 2001 and changed to occupy an area of 2.47 km2 in 2011. Gravelly clay loam decreased from 42 km2 in 2001 to 32 km2 in 2011. Gravelly sandy clay loam too decreased from 7.99 km2 in 2001 to 6.23 km2 in 2011. Similarly, sandy clay loam changed from 8.06 km2 in 2001 to 7.75 km2 in 2011. Extent of water bodies
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Fig. 10 Pixel based EVUs for the year a 2001 and b 2011; Micro-watershed-based EVUs for the year, c 2001 and d 2011 Table 5 Erosion vulnerability units (EVUs) of the watershed Erosion vulnerability units (EVUs)
Severity class
Range of erosion (tha-1 yr-1)
Soil conservation priorities
1
Very severe
[40
Special soil and water conservation measures required
2
Severe
20–40
High priority for soil conservation
3
Moderate
10–20
Medium priority for soil conservation
4
Low
5–10
Less priority for soil conservation
5
Very low
0–5
Much less priority for soil conservation
decreased from 6.54 km2 in 2001 to 3.66 km2 in 2011. Overall decrease in the spatial extent of soil covers is largely believed to have taken place due to relative
increase in the area under built-up category, which increased considerably over the years 2001 to 2011. According to Anonymous (1990), bare soil and vegetation such as grasses and shrubs are replaced by impervious surfaces when agricultural and vegetative areas become urbanized. Thus, due to urbanization, impervious area increases and can result in two types of changes in the watershed hydrology: (i) due to increased impervious area, rainfall does not percolate into the soil and (ii) as the resistance is reduced, peak flow increases and runoff reaches the channels much faster. With an increase in the peak rate, flow velocity is faster and water levels are higher in the streams (Anonymous, 1986). Human use of land in the urban environment has increased both the magnitude and frequency of floods (Stein 2005). Therefore, the change in built-up area was considered as a significant LULC change factor, in the intensification of overall soil erosion in the catchment.
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Depending upon the range of erosion, five severity classes were delineated and designated as the EVUs. Thus, the EVUs identified were very low (\5 t ha-1 yr-1), low (5–10 t ha-1 yr-1), moderate (10–20 t ha-1 yr-1), severe (20–40 t ha-1 yr-1) and very severe ([40 t ha-1 yr-1). Such EVUs based on the mean erosions for both the reference years were mapped and classified. This classification took into consideration the severity and range of soil erosion rate. The pixel-based values were used to calculate mean annual soil loss in terms of EVUs for the years 2001 and 2011 (Fig. 10a, b). For better information on change in the spatial extent and intensity of soil erosion, zonal statistics were used to create comparative micro-watershed level soil erosion maps (Fig. 10c, d). Central portion of some of the microwatersheds showed even decrease in soil erosion as compared to 2001. The reason for this could be an increase in the built-up area with less availability of barren land vulnerable to usual erosion processes. EVUs were thus established for this watershed along with their characteristic severity classes, range of erosion and the corresponding prioritization toward soil conservation (Table 5). These results showed that certain areas within the EVUs represented very low–tosevere condition of soil erosion in 2001 and the same areas showed signs of increasing erosion rate in the recent reference period of the year 2011. Due to this, there was an increase in overall erosion surpassing the severe range over to the very severe range with erosion vulnerability of [40 t ha-1 yr-1. Fig. 11 Change in soil erosion status under different LULC over the years 2001 and 2011
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Change in the erosion potential and LULC transition further revealed the cause–effect relationship between the LULC change dynamics and the micro-watershed level EVUs. Using hydrology tools of Arc GIS software and the ASTER GDEM, micro-watersheds of this watershed were extracted, which numbered approximately 500. These micro-watersheds showed varying severity of erosions as represented by the EVUs. Subsequent changes in soil erosion potential of the watershed were thus inferred by superimposition of the EVU and LULC change maps. Results of changes in soil erosion status under different LULC over the reference years are shown in Fig. 11. During the year 2001, more than 70 % of the soil under barren land underwent severe-to-moderate levels of erosion. These areas mostly fell under EVU1 and EVU3, respectively. During the year 2011, very severe soil erosion had also set in corresponding to EVU1 under the barren land category. This might have led to the increase in the corresponding area under very severe-to-moderate erosion ranging from EVU1 to EVU3 going up from 70 % in the year 2001 to 75 % in the year 2011. Such pattern of intensification of very severe erosion under EVU1 was also observed for other LULC classes such as agricultural land, built-up land and dense vegetation. Most notable change occurred in the barren land category in which more than 20 % area was found to have come under EVU1 with very severe soil erosion. Moderate soil erosion of EVU3 range was observed to have more than doubled with the
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intensification of severe and very severe soil erosions of EVU2 and EVU3 categories, especially in case of dense vegetation and agricultural land. Areas of land under moderate-to-severe soil erosions of EVU3 and EVU2 ranges under all other LULC categories increased with the intensification of very severe erosion of EVU1 range. Very low-to-low soil erosions of EVU4 and EVU5 ranges decreased under all the categories of LULC except that of the built-up category. This reflected an increase in EVU3, EVU4 and EVU5 corresponding to moderate, severe and very severe erosion ranges, respectively. Thus, there are requirements of much efficient management and soil conservation priorities, such as medium and high priority, and special soil and water conservation measures, respectively, for corresponding EVUs at micro-watershed levels.
Conclusions Soil erosion as a water-induced phenomenon is a significant problem being reported from various parts of the world. There is less information available on the factors responsible for soil erosion vulnerability, which necessitates more area-specific studies. Geospatial tools and techniques used in this study greatly aided the delineation of erosion vulnerability of this watershed within Upper Subarnarekha River Basin. Assessment of erosion with due consideration of distinct severity classes depicted by EVUs could be established. EVU categories established for this watershed ranged over five severity classes, namely very low (\5 t ha-1 yr-1), low (5–10 t ha-1 yr-1), moderate (10–20 t ha-1 yr-1), severe (20–40 t ha-1 yr-1) and very severe ([40 t ha-1 yr-1). Thus, EVUs derived were based on the soil erosion values obtained across various land use classes of the watershed. Such EVU categories may vary from one micro-watershed to another, even within the basin and such information generated using geospatial techniques may be considered significant for watershed management and planning. Furthermore, such delineation of EVUs should also hold good for contiguous watersheds of the region with similar host terrain conditions. This watershed is situated in the vicinity of Ranchi, the capital city of the Indian state of Jharkhand, which is undergoing rapid urbanization due to its relatively new status as a state capital. Therefore, anthropogenic activities are the dominating drivers of LULC changes. This pertains to the last 10 years, leading to changes in the soil erosion potential both positive and negative as well as mixed behavior. Based on the satellite data of the years 2001 and 2011, increase in the areas under urban or built-up land were observed at the expense of other types of productive land covers such as agriculture and vegetation. Overall effects of the LULC change over the period 2001–2011 seem to
have a negative impact on the watershed, which is reflected by the shifting of EVUs under less soil erosion ranges to the EVUs under higher soil erosion ranges. Even though overall area of the watershed shows increase in impervious area, but the increased rate of erosion was noticed due to a variety of associated anthropogenic implications. Impervious surfaces are likely to concentrate and accelerate water accumulation and flow. An understanding of the dynamics of erosion in a watershed like this certainly requires the appreciation of the impervious or paved and unpaved areas owing to the urbanization process. Even if thoughts on urbanization aspects of the watersheds like this are spared, EVUs established for this watershed may provide a good insight into the problem at micro-watershed levels toward better watershed management and prioritization. Thus, the techniques adopted in this study have the potential to be extended to other watersheds as well to manage them sustainably with better planning and conservation approach. Acknowledgments The first author is thankful to the CIFRI, Indian Council of Agricultural Research (ICAR), Kolkata, and also to the Birla Institute of Technology (BIT), Mesra for all the facilities made available and availed for the work as a Research Scholar. Dr. S. K. Sahu and Miss Manisha Bhor of CIFRI (ICAR), Kolkata, are acknowledged for their time to time help. Satellite digital data available from USGS Global Land Cover Facility and used in this study is also duly acknowledged. Authors gratefully acknowledge the anonymous reviewers for providing their critical comments to improve the quality of this manuscript. Authors are also thankful to Dr. Gunter Doerhoefer, Editor-in-Chief, of the journal toward improvements in the manuscript.
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