Water Resour Manage (2008) 22:165–190 DOI 10.1007/s11269-006-9149-5
SCS-CN-based Continuous Simulation Model for Hydrologic Forecasting K. Geetha & S. K. Mishra & T. I. Eldho & A. K. Rastogi & R. P. Pandey
Received: 19 March 2006 / Accepted: 22 December 2006 / Published online: 1 February 2007 # Springer Science + Business Media B.V. 2007
Abstract A new lumped conceptual model based on the Soil Conservation Service Curve Number (SCS-CN) concept has been proposed in this paper for long-term hydrologic simulation and it has been tested using the data of five catchments from different climatic and geographic settings of India. When compared with the Mishra et al. (2005) model based on variable source area (VSA) concept, the proposed model performed better in all applications. Both the models however exhibited a better match between the simulated and observed runoff in high runoff producing watersheds than did in low runoff producing catchments. Using the results of the proposed model, dominant/dormant processes involved in watershed’s runoff generating mechanism have also been identified. The presented model is found useful in the continuous simulation of rainfall–runoff process in watersheds. Keywords hydrological forecasting . long-term hydrologic simulation . saturation excess overland flow . streamflow . rainfall–runoff . variable source area . curve number . antecedent moisture Nomenclature P total rainfall initial abstraction Ia effective rainfall Pe infiltration at any time ‘t’ in SCS-CN-based model Ft K. Geetha : T. I. Eldho (*) : A. K. Rastogi Department of Civil Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai 400 076, India e-mail:
[email protected] S. K. Mishra Indian Institute of Technology, Roorkee, 247 667 Uttaranchal, India R. P. Pandey National Institute of Hydrology, Roorkee, 247 667 Uttaranchal, India
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ROt DRt PRt DSPt DPRt SROt THRt BFt TROt EVPt EVt TRt ETt SMS GWS SCS-CN
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rainfall excess (surface runoff) at any time ‘t’ for 5-days from beginning of storm subsoil drainage at any time ‘t’ percolation at any time ‘t’ deep seepage at any time ‘t’ in SCS-CN-based model deep percolation at any time ‘t’ surface runoff at any time ‘t’, if t>5-days throughflow at any time ‘t’ in SCS-CN-based model base flow at any time ‘t’ in SCS-CN-based model total runoff at any time ‘t’ in SCS-CN-based model potential evaporation at any time ‘t’ evaporation at any time ‘t’ transpiration at any time ‘t’ actual evapotranspiration at any time ‘t’ in SCS-CN-based model soil moisture store ground water store Soil Conservation Service-Curve Number
1 Introduction The simulation of rainfall-generated runoff is very important in various activities of water resources development and management such as flood control and its management, irrigation scheduling, design of irrigation and drainage works, design of hydraulic structures, and hydro-power generation etc. Ironically, determining a robust relationship between rainfall and runoff for a watershed has been one of the most important problems for hydrologists, engineers, and agriculturists since its first documentation by P. Perrault (In: Mishra and Singh 2003) about 325 years ago. The process of transformation of rainfall to runoff is highly complex, dynamic, non-linear, and exhibits temporal and spatial variability, further affected by many and often interrelated physical factors. However an understanding of various hydrologic variations (spatial and temporal) over long periods is necessary for identification of these complex and heterogeneous watershed characteristics. There exists a multitude of watershed models of varying complexity in the hydrologic literature (Singh 1989, 1995; Singh et al. 2006; Limbrunner et al. 2006; www.usbr.gov/ pmts/rivers/html/index.html). Since the development of Stanford watershed model (Crawford and Linsley 1966), numerous operational, lumped, conceptual models have been developed. Among them the Boughton model (Boughton 1966, 1968), Kentucky watershed model (Liou 1970; James 1970, 1972), Institute of Hydrology model (Nash and Sutcliffe 1970; Mandeville et al. 1970), Tank model (Sugawara et al. 1984), HYDROLOG (Poter and McMahon 1976), MODHYDROLOG (Chiew and McMahon 1994), HRUT (Yao et al. 1996) are worth citing. The variable source area (VSA) concept coupled with concepts of topographic index and curve number (described below) has been quite useful in adaptable runoff estimation (Choi et al. 2002). Beven and Kirkby (1979) suggested that a conceptual model that can simulate the variable source areas could be used for long-term water yield estimation which incorporates soil moisture replenishment, depletion, and redistribution for the dynamic variation of areas during and after the storm contributing to direct runoff. The TOPMODEL (Beven and Kirkby 1979; Beven et al. 1984, 1995) which is a continuous hydrologic model does however not account for the spatial variability of land use and soil characteristics.
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The other widely used simple technique is the Soil Conservation Service Curve Number (SCS-CN) method and is now known as Natural Resource Conservation (NRCS) (USDA 1986). It was first developed by the United States Department of Agriculture (USDA) in 1954 to transform rainfall to direct surface runoff. Ponce and Hawkins (1996) critically examined the SCS-CN method and indicated several advantages over other methods. Since 1996, a significant amount of literature is published and several recent articles have reviewed the methodology in detail, and the work still continues, as for example, the work of Michel et al. (2005). Recent advances in computer environments and availability of Geographic Information System (GIS) tools have made a drastic change in the hydrological modeling and development with the application of SCS-CN technique (Nageshwar et al. 1992; Arnold et al. 1993; Heaney et al. 2001). Though the model has its own limitations (Ponce and Hawkins 1996; Choi et al. 2002; Mishra and Singh 2003; Michel et al. 2005), it has been widely used in numerous models, including AGNPS (Heaney et al. 2001) for single events; long term hydrological impact assessment model, L-THIA (Harbor 1994; Bhaduri 1998); SWAT model (Spruill et al. 2000); CELTHYM (Choi et al. 2002) etc. Besides the application of the SCS-CN model in long-term simulation (Huber et al. 1976; Williams and LaSeur 1976; Knisel 1980; Soni and Mishra 1985; Woodward and Gbuerek 1992; Mishra et al. 1998; Mishra and Singh 1999, 2004a; Mishra 2000), this method has also been employed for determination of infiltration and runoff rates (Mishra 1998; Mishra and Singh 2002a,b, 2004b). The original SCS-CN method was designed to predict direct (surface) runoff, as an infiltration-excess overland flow model. It assumes that only one process is responsible for producing streamflow over the entire watershed area. The SCS-CN supports Horton’s overland flow mechanism to compute the surface runoff, in contrast to the VSA-Based model that assumes saturation-excess overland flow mechanism from dynamic source areas. In VSABased model, the catchment is assumed to be partitioned as source areas and non-source areas in which infiltration occurs through non-source areas. The original SCS-CN method computes the direct runoff by considering only the available rainfall on the current day without taking into account of the effect of the moisture available prior to the storm. On the other hand, the curve numbers are sensitive to antecedent conditions (Ponce 1989). The original method does not contain any expression for time and ignores the impact of rainfall intensity and its temporal distribution. There is no explicit provision for spatial scale effects also. The other demerits of the original method are the absence of clear guidance of how to vary the antecedent moisture conditions and the fixing of initial abstraction ratio 0.2, pre-empting a regionalization based on geological and climatic settings (Choi et al. 2002). Besides, since the conventional method was developed for agricultural sites, it works best on these sites, fairly on range sites, and poorly on forest sites. Here in this paper, an attempt has been made to improve the existing SCS-CN model by eliminating the demerits of the existing model. With the improved understanding available on both the variable source area and SCS-CN concepts, it is essential to apply these models to long-term (daily) data from different climatic and geographic settings and compare their performance. Thus, the main objectives of this paper are to (a) conceptualize and develop a lumped model based on the SCS-CN technique modified for accounting of the antecedent moisture effect and (b) compare this model performance with another lumped conceptual model based on variable source area (VSA) theory (Mishra et al. 2005) on the data of five Indian watersheds of Cauvery in Karnataka, Narmada in Madhya Pradesh, and Ulhas in Maharashtra. The modified SCS-CN-based lumped model considers various hydrologic components involved in the runoff generation mechanisms and takes into account of temporal variations of curve number.
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The proposed model differs from the original model, as in daily flow simulation, the original SCS-CN method is used to compute the direct surface runoff considering the rainfall of the current day utilizing the CN-values corresponding to antecedent 5-day AMC, allowing unrealistic sudden quantum jumps in CN-variation. Secondly, the value of initial abstraction coefficient is fixed as 0.2, which has shown to be varying in literature (For example, Mishra and Singh 2003). The proposed long term hydrologic model obviates these limitations and is capable of simulating, other than direct surface runoff, the total streamflow and its components such as surface runoff, throughflow, and base flow which is conceptualized to have two different moisture stores, i.e. soil moisture store and ground water store. This continuous simulation model considers a daily time step interval for analysis. Thus, the present version is a significant enhancement over the previous ones utilizing original SCS-CN method. This long term hydrologic model is capable of simulating streamflow and its components such as surface runoff, throughflow, and base flow and is also conceptualized to have two different moisture stores, i.e. soil moisture store and ground water store. 2 Existing SCS-CN Model The original SCS-CN method was documented in Section 4 of the National Engineering Handbook (NEH) in 1956. The document has since been revised subsequently in 1964, 1965, 1971, 1972, 1985, 1993 (In: Mishra and Singh 2003), and 2004 (SCS 2004). The method which is derived to compute the surface runoff from rainfall in small agricultural watersheds is based on water balance equation and the two hypotheses, respectively, as follows (Mishra and Singh 1999, 2003): P ¼ Ia þ F þ Q
ð1Þ
Q F ¼ P Ia S
ð2Þ
Ia ¼ lS
ð3Þ
where P=total precipitation; Ia =initial abstraction; F=cumulative infiltration; Q=direct runoff; S=potential maximum retention or infiltration; l=initial abstraction coefficient. But l varies in the range of 0 to ∝ and is assumed as a standard value of 0.2 in usual practical applications (Mishra and Singh 1999, 2004b).
2.1 Computation of Surface Runoff Combination of Eq. 1, 2, and 3 leads to the following popular form of the SCS-CN method to compute daily direct (surface) runoff ROt Eq. 4 with time t as subscript as
ROt ¼
Pe2t Pet þ St
ð4Þ
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where Pe(t) is the effective rainfall at any time ‘t’ excluding the initial abstraction; PeðtÞ ¼ Pt IaðtÞ
St ¼
ð5Þ
25; 400 254 CNt
ð6Þ
where CNt is the curve number at any time‘t’. Here, Pe(t) ≥0; else ROt =0. In Eq. 6 St varies from 0 to ∝ , which can be mapped onto a non-dimensional curve number CN with a more appealing range of 0 to 100 as in Eq. 6; and all dimensions are in millimetres.
3 Formulation of Continuous Simulation Model The present model formulation incorporates the SCS-CN concept revised for rainfalldependent initial abstraction and quantification of flows adopting various flow paths in streamflow generation, such as (1) Surface runoff, (2) Throughflow and (3) Base flow (Fig. 1a). This algorithm operates on daily time basis and, therefore, requires daily data of rainfall and evaporation as input to explain the physical behavior of the catchment. The observed runoff is used for model evaluation. A complete description of individual components of the proposed model as follows: 3.1 Initial Abstraction Initial abstraction is considered as a short term loss before ponding such as interception, infiltration, surface storage (Ponce and Hawkins 1996; Mishra and Singh 2003). Here it is Fig. 1 Schematic diagram of SCS-CN-based lumped conceptual rainfall-runoff model
P-Ia
ETt TRt
EVt
SROt
Ft SMS DRt THRt
PRt
GWS DSPt BFt TROt
DPRt
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assumed that this loss is a fraction of the possible retention in the soil and is computed as: IaðtÞ ¼ lSt
if t 5 days
ð7Þ
Here, l is taken as 0.2. Physically this means that for a given storm, 20% of the potential maximum water retention is the initial abstraction before runoff begins (Singh 1992). Otherwise a Pt ð8Þ IaðtÞ ¼ l1 St Pt þ St Here l1 and a are the coefficient and exponent of the initial abstraction which are to be optimised. 3.1.1 Antecedent Rainfall In literature, the term antecedent varies from previous 5 to 30 days (SCS 1971; Singh 1992; Mishra and Singh 2003). However no explicit guideline is available to vary the soil moisture with the antecedent rainfall of certain duration. Since the NEH-4 (SCS 1971) uses 5-day rainfall based on the exhaustive field investigations, this duration of 5 days was retained. In this model, for the first 5 days beginning from the starting day of simulation (June 1– June 5, in this study), curve number CN is taken as CN0 and as the day advances, CN varies with respect to antecedent moisture amount, AM, based on the antecedent rainfall (ANTRF) as: ANTRFt ¼ Pðt1Þ þ Pðt2Þ þ Pðt2Þ þ Pt3 þ Pðt4Þ þ Pðt5Þ
ð9Þ
where t is the day under consideration and P is the rainfall of the respective day. 3.1.2 Antecedent Moisture The initial moisture available in the soil prior to storm plays a vital role in the estimation of runoff (Mishra and Singh 2002a) as curve number CN variability is primarily attributed to antecedent moisture amount rather than the antecedent moisture conditions (SCS 1971; Mishra and Singh 2003) which may lead to sudden jumps in daily curve number values. This model assumes that the current space available for water retention St is constant for first 5 days of simulation and hence CNt =CN0. Using Eq. 6 St can be computed from curve number CN0 of the first day which is determined by optimisation. When the number of days exceeds 5, the antecedent moisture, representing the initial moisture available in the watershed on the day under consideration (AMt), can be computed as follows: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi AMt ¼ b ANTRFt ð10Þ Here, b is the coefficient of antecedent moisture to be determined by optimisation. Then, St is modified as St ¼
ðSt Þ2 ðAMt þ St Þ
ð11Þ
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Here in this model, it is considered that daily antecedent moisture amount AMt varies with respect to antecedent rainfall (Eq. 10) and hence the daily possible water retention of the soil is computed using Eq. 11. 3.2 Rainfall Excess The amount of rainfall (P) reaching on the ground after the initial losses (Ia) is termed as effective rainfall (Pe) and this is available for initiating various other processes in the hydrologic cycle. The effective rainfall (Pe) is assumed to be partitioned as surface runoff or rainfall excess (RO) and infiltration (F) as stated in Eq. 1. Using the daily effective rainfall (Pet), the daily rainfall excess ROt can be computed by using Eq. 4 for the first 5days of simulation, only if rainfall P exceeds initial abstraction (Ia), it is zero otherwise. 3.2.1 Routing of Rainfall Excess When the number of days exceeds 5, to transform the surface runoff that is produced at the outlet of the basin, the rainfall excess ROt (Eq. 4) is routed using a single linear reservoir concept, as follows (Nash 1957; Mishra and Singh 2003): SROt ¼ C0 ROt þ C1 ROðt1Þ þ C2 SROðt1Þ
ð12Þ
where C0 ¼
ð1=K Þ 2 þ ð1=K Þ
C1 ¼ C0
C2 ¼
2 ð1=K Þ 2 þ ð1=KÞ
ð13aÞ
ð13bÞ
ð13cÞ
where K is the storage coefficient. 3.3 Infiltration The amount of water reaching the ground after initial abstraction and not produced as direct surface runoff is assumed to infiltrate into the upper soil. It is modelled as:
Ft ¼ Pt IaðtÞ ROðtÞ
ð14Þ
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3.4 Evapotranspiration The amount of water goes back or lost to the atmosphere is in the form of evapotranspiration ETt and can be obtained by the summation of daily evaporation from the water bodies and transpiration from the soil zone in the watershed. 3.4.1 Evaporation The daily evaporation EVt is computed as follows: EVt ¼ PANC EVPt
ð15Þ
where EVPt is the potential evaporation based on the field data and PANC is the pan coefficient, assumed as 0.8 for June–September and 0.6 for October–November in this study (Project Report 1978).
3.4.2 Transpiration Transpiration from the soil zone is considered as a function of water content available in the soil store above the wilting point of the soil (Putty and Prasad 1994, 2000; Mishra et al. 2005). The transpiration is computed as: TRt ¼ C1 ðSabs St qw Þ
ð16Þ
where C1 =coefficient of transpiration from soil zone, θw =wilting point of the soil, Sabs =the maximum possible water retention, and St =possible water retention on tth day. The total actual evapotranspiration is taken as the sum of evaporation and transpiration as follows: ETt ¼ EVt þ TRt
ð17Þ
3.5 Drainage The term drainage is used as the outflow from a linear reservoir (Nash 1957) only when the moisture content in the soil zone increases and exceeds the field capacity θf (Putty and Prasad 2000; Mishra et al. 2005) as: DRt ¼ C2 ðSabs St qf Þ
ð18Þ
where C2= subsoil drainage coefficient, Sabs =maximum potential water retention, St = possible water retention on tth day, DRt =drainage rate at time‘t’, and θf =field capacity of the soil.
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3.6 Throughflow or Interflow The outflow from the unsaturated soil store is partitioned into two components: (1) subsurface flow in lateral direction and (2) vertical percolation into ground water zone. The former component representing the through flow is taken as a fraction of the above drainage rate (Putty and Prasad 1994, 2000): ð19Þ THRt ¼ C3 DRt where THRt =throughflow at time ‘t’ and C3 =unsaturated soil zone runoff coefficient. 3.7 Percolation The outflow in the vertical direction from the unsaturated zone meets the ground water store due to the permeability of the soil. This percolated amount of water is considered as a part of drainage, and it is estimated as (Putty and Prasad 1994, 2000; Mishra et al. 2005): PRt ¼ ð1 C3 Þ DRt
ð20Þ
where PRt=percolation at time ‘t’. 3.8 Deep Seepage The saturated store is considered as a non-linear reservoir and from this saturated store, outflow occurs at an exponential rate in the form of deep seepage. As the saturated store is considered as a non-linear store, the formulation for the deep seepage is made as an exponential function of the percolation. This is modeled as follows: DSPt ¼ ðPRt ÞE
ð21Þ
where DSPt =deep seepage at any time ‘t’ and E=exponent of ground water zone. Deep seepage can travel in lateral direction as well as vertical direction through the saturated store. This seepage is again bifurcated into two components: (1) active ground water flow (base flow) and (2) inactive ground water flow (deep percolation) into the aquifers. 3.9 Base Flow The base flow of a watershed is the ground water release from a catchment in a stream. This active ground water flow which is also known as delayed flow can be modeled as outflow from a non-linear storage in the form of base flow (BFt) as follows: BFt ¼ BCOEF DSPt
ð22Þ
where BCOEF=ground water zone runoff coefficient 3.10 Deep Percolation The inactive ground water flow into aquifers is termed as deep percolation, occurs from the saturated ground water zone in vertical direction, and is considered as a loss from the saturated store which is modeled as: DPRt ¼ ð1 BCOFFÞ DSPt
ð23Þ
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where DPRt =deep percolation at any ‘t’ and BCOEF=ground water zone runoff coefficient. Here, it is worth emphasizing that the proposed model considers deep seepage which is partitioned into two components, base flow and deep percolation. On the other hand, both the Putty and Prasad (1994, 2000) and Kentucky (James 1972; Singh 1989) models do not account for deep seepage and deep percolation. 3.11 Total Stream Flow The total stream flow (TROt) on a day t, is obtained as the sum of the above three components, surface runoff, throughflow, and base flow (Eqs. 4 or 12, 19, and 22). TROt ¼ ROt þ THRt þ BFt
if t 5 days
ð24aÞ
TROt ¼ SROt þ THRt þ BFt
if t > 5 days
ð24bÞ
3.11.1 Water Retention Budgeting The computation of daily water retention storage or soil moisture budgeting is essential in a daily hydrologic simulation. This SCS-CN-based model represents a soil-water balance model. The current space available for retention of water St is again modified by taking into account the evapotranspiration loss, drainage from the soil moisture zone, and daily infiltration to the unsaturated store as: St ¼ Sðt1Þ Fðt1Þ þ ETðt1Þ þ DRðt1Þ
ð25Þ
where S(t−1) is the previous day potential maximum retention (mm); ET(t−1) is the previous day evapotranspiration (mm); DR(t−1) is the drainage on the previous day; F(t−1) is the previous day infiltration (mm), computed using water balance equation: Fðt1Þ ¼ Pðt1Þ Iaðt1Þ ROðt1Þ
ð26Þ
Here, if Pe(t) ≥0, F≥0.
4 Development of Continuous Simulation Model The proposed long-term hydrologic simulation model (Fig. 1) is developed for describing watershed hydrology by considering temporal and spatial variations of various processes involved in the runoff generation mechanism and also by incorporating modified soil conservation service curve number (SCS-CN) technique as well as storage concepts to represent the catchment response in a better way. This modified SCS-CN based lumped model that captures the relevant catchment features requires 13 parameters, viz., CN0, l1, a, b, K, C1, C2, C3, Sabs, θf, θw, BCOEF, and
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E to derive an acceptable model output. Commonly, a close fit between calculated and observed variables is possible for models with a high number of parameters even if the model assumptions are false (Grayson et al. 1992; Beven 1997). In contrast to the studies showing less number of parameters is needed to establish rainfall–runoff relationship (Jakeman and Hornberger 1993), here the number of parameters involved in the model is comparatively large, but it is at the gain of significant higher efficiency and it generates not only streamflow but also its components, a distinctive feature. It is notable that the presented model requires easily available rainfall and evaporation in order to generate streamflow and its components. Here the parameters involved in the model are determined using non-linear Marquardt algorithm (In: Mishra and Singh 2003), coupled with trial and error, utilising the objective function of minimising errors between the computed and observed data or maximising model efficiency (Nash and Sutcliffe 1970). The ranges and initial values selected for the optimizations are presented in Table 1. Notably, initial estimates of the parameters fixed by trial-and-error need not be provided. A comparison has also been made between the present model based on SCS-CN concept and a model based on variable source area (VSA) theory (Mishra et al. 2005). This 3component VSA-based model assumes three different stores of moisture as interception
Table 1 Ranges and initial estimates of parameters (SCS-CN-Based model) Sl. No.
Parameters
1
CN0
2
BCOEF
3
K
4
l1
5
a
6
b
7
E
8
C1
9
C2
10
C3
11
Sabs
12
θf
13
θw
Range/Initial value
Range Initial value Range Initial value Range Initial value Range Initial value Range Initial value Range Initial value Range Initial value Range Initial value Range Initial value Range Initial value Range Initial value Range Initial value Range Initial value
Catchment Hemavati
Hridaynagar
Manot
Mohegaon
Kalu
0–100 – 0–1.0 – 0.1–2.0 0.5 0.0–5.0 0.2 0–10.0 0.01 0–3.0 0.01 0–1.0 – 0–1.0 – 0–1.0 0.05 0–1.0 0.05 500–1500 – 100–1000 – 40–120 –
0–100 – 0–1.0 – 0.1–5.0 0.5 0–5.0 0.02 0–5.0 0.01 0–3.0 0.01 0–1.0 – 0–1.0 – 0–1.0 0.1 0–1.0 0.05 300–1000 – 100–500 – 40–120 –
0–100 – 0–1.0 – 0.1–1.0 0.5 0–10.0 0.02 0–10.0 0.01 0–1.0 0.01 0–2.0 – 0–1.0 – 0–1.0 0.1 0–1.0 0.05 300–1000 – 100–500 – 40–120 –
0–100 – 0–1.0 – 0.001–10 0.5 0–10.0 0.02 0–10.0 0.01 0–10.0 0.01 0–2.0 – 0–1.0 – 0–1.0 0.1 0–1.0 – 300–1000 – 100–1000 – 40–120 –
0–100 – 0–1.0 – 0.1–1.0 0.5 0–1.0 0.05 0–1.0 0.01 0–1.0 0.001 0–2.0 – 0–1.0 – 0–1.0 0.05 0–1.0 – 500–1500 – 100–1200 – 40–120 –
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Fig. 2 a Drainage map of Hemavati catchment. b Index map of Narmada at Manot, Banjar at Hridaynagar, and Burhner at Mohegoan watersheds. c Drainage map of Kalu catchment
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Fig. 2 (Continued)
store, soil moisture store, and ground water store. The model quantifies daily as well as annual streamflow and its components i.e. source area runoff, throughflow and base flow. The main difference between the SCS-CN-based model and VSA-based model is that the former is an infiltration-excess model and assumes the surface runoff to be produced from the entire catchment whereas the latter produces it from source areas only, i.e. certain dynamic contributing areas varying with storm intensity. Furthermore, the former assumes the surface runoff to be produced due to infiltration-excess overland flow, similar to Hortonian overland flow whereas the latter produces the surface runoff due to saturationexcess overland flow. The latter model is a modification of the Kentucky watershed model (James 1972) and SAHYADRI model (Putty and Prasad 1994, 2000), as these models do not account for the deep seepage and deep percolation.
5 Case Study Watersheds The above proposed model is applied to different catchments falling under different climatic and geographic settings of India. The daily monsoon (June–November) data of study catchments (Fig. 2a, b, c), Hemavati, a tributary of River Cauvery in Karnataka State, Hridaynagar, Manot, and Mohegaon catchments, tributaries of River Narmada in Madhya Pradesh, and Kalu catchment, a tributary of River Ulhas, in Maharashtra State of India, observed at five gauging stations, are used for the analysis. Details of these catchments are presented in Table 2.
Red loamy soil and red sandy soil 1,240–890
Soil
Elevation (m) above m.s.l Average annual rainfall 2,972.00 (mm) Calibration–Monsoon 1974–1976 (3 years) Period Validation–Monsoon 1977–1978 (2 years) Period
Land use
Cauvery Karnataka Chikmanglur 600.00 12°55′ to 13°11′ N 75°29′ to 75°51′ E Low land, semi hilly and hilly Forest – 12% Coffee plantation – 29% Agriculture land – 59%
Hemavati
Catchment
River State District Area Latitude Longitude Topography
Description
1,596.00 1981–1985 (5 years) 1986–1989 (4 years)
1981–1985 (5 years)
1986–1989 (4 years)
1986–1989 (4 years)
1981–1985 (5 years)
1,547.00
Red and yellow silty loam and silty clay loam 900–509
Forest – 58% Agriculture – 42%
Forest – 35% Cultivation – 52% Waste land – 13% Red, yellow, and medium black soil 450–1,110
Narmada Madhya Pradesh Mandla 4,661.00 22° 32′ N 81° 22′ E Both flat and undulating lands
Mohegaon
Narmada Madhya Pradesh Shahdol 5,032.00 22°26′ to 23°18′ N 80°24′ to 81°47′ E Hilly
Manot
1,178.00
600–372
Narmada Madhya Pradesh Durg 3,370.00 21° 42′ N 80° 50′ E both flat and undulating lands Forest – 65% Agriculture – 29% Degraded land water bodies – 6% Black to mixed red soil
Hridaynagar
Table 2 Data used for model calibration and validation
1993 (1 year)
1990–1992 (3 years)
2,450.00
Silty loam and sandy loam 1,200
Forest – 50% Cultivation – 50%
Ulhas Maharashtra Thane 224.00 19° 17′ to 19° 26′N 73°36′ to 73° 49′ E Hilly
Kalu
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(a)
Hemavati catchment: River Hemavati is a tributary of River Cauvery, originating in Ballaiarayanadurga in the Western Ghats in Mundgiri taluk of Chikmanglur district in Karnataka State (Fig. 2a). It traverses a total length about 55.13 km. upto Sakleshpur. Agriculture and plantation are the major industries of the basin. Soils in the forest area and coffee plantations are greyish due to high humus content. (b) Narmada upto Manot: River Narmada (Table 2) rises from Amarkantak plateau of Maikala range in Shahdol district in Madhya Pradesh at an elevation of about 1059 m above mean sea level. The length of the river Narmada from its origin up to Manot is about 269 km (Fig. 2b). It has continental type of climate classified as sub-tropical and sub-humid. It is very hot in summer and cold in winter. (c) Banjar up to Hridaynagar: The Banjar River (Fig. 2b), a tributary of Narmada in its upper reaches, rises from the Satpura range in Durg district of Madhya Pradesh near Rampur village at an elevation of 600 m and the elevation drops from 600 to 372 m at Hridaynagar gauging site (Table 2). Climate of the basin can be classified as subtropical sub-humid and about 90% of the annual rainfall is received during monsoon season (June–October). (d) Burhner up to Mohegaon: The Burhner river (Mohegaon catchment), a tributary of Narmada River rises in the Maikala range, south-east of Gwara village in the Mandla district of Madhya Pradesh at an elevation of about 900 m. It flows in westerly direction for a total length of 177 km to join the Narmada near Manot. The elevation at Mohegaon gauging site drops to 509 m. and climate of the basin can be classified as sub-tropical and sub-humid. (e) Kalu catchment: River Kalu is a tributary of Ulhas River in the Thane District of Konkan Region in Maharashtra (Fig. 2c). It originates near Harichandragad in Murbad Taluka of Thane District at an elevation of 1200 m above mean sea level (Table 2). Most of the rains are received during June–October. Existing crop pattern of the cultivation covers 46% paddy, 16% nanchani vari, 3% pulses, and 35% grass.
6 Model Application The above described model employs the revised SCS-CN model incorporating antecedent moisture amount, AMA, to calculate the space available for water retention; and updates the water retention store daily using evapotranspiration, drainage from soil moisture store, and infiltration to soil moisture store. Its application requires daily data of rainfall and evaporation. It has been applied to the daily data of monsoon period (June–November) of the above catchments. The details of study watersheds and the record of hydrologic data used in this analysis are presented in Table 2. The optimal estimates of model parameters (Mishra et al. 2005) were obtained by using non-linear Marquardt algorithm (In: Mishra and Singh 2003) coupled with trial-anderror. Table 3 presents the model efficiency along with runoff coefficients of the catchment. It is seen that Hridaynagar catchment shows the least runoff coefficient of 0.262 and behaves as dry catchment (Gan et al. 1997). The catchments of Kalu and Hemavati can be classified as high runoff producing catchments with the coefficient values of 0.964 and 0.803, respectively. The runoff coefficients for Manot and Mohegaon catchments are 0.475 and 0.362, respectively, describing them to lie in the intermediate category of dry and wet.
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Table 3 Estimates of model parameters and model efficiency (SCS-CN-based model) Sl. No
Parameter
Hemavati Hridaynagar Manot Mohegaon Data Length – Monsoon Period (June–November)
Kalu
Cal – 3 years Cal – 5 years Cal – 5 years Cal – 5 years Calib – 3 years Val – 2 years Val – 4 years Val – 4 years Val – 4 years Valid – 1 year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
CN0 K l1 a b C1 C2 C3 BCOEF E Sabs θf θw Efficiency Calibration Efficiency Validation Runoff Coefficient
35.0 1.039 0.551 4.174 0.651 3.75 0.173 0.898 0.195 0.63 1,350.0 800.0 100.0 84.43%
69.0 1.007 1.822 1.633 0.681 4.2 0.041 0.135 0.62 0.91 500.0 350.0 80.0 58.84%
61.0 0.123 4.937 6.851 0.990 8.2 0.096 0.305 0.19 1.49 500.0 350.0 90.0 74.36%
30.0 0.016 5.566 3.074 5.627 17.36 0.020 0.790 0.45 1.04 700.0 500.0 80.0 73.38%
51.0 0.548 0.289 0.794 0.720 2.775 0.046 0.200 0.740 1.35 1,200.0 950.0 90.0 67.86%
87.70%
47.73%
66.51%
37.0%
84.0%
0.803
0.262
0.475
0.372
0.964
The model yields maximum efficiency of 84 and 88% in calibration and validation, respectively, in Hemavati catchment whereas Hridaynagar catchment produces the least efficiencies of 59 and 48%, respectively. The highest efficiency reveals that the model is efficacious to high runoff producing Hemavati catchment. The lower the efficiency, the higher the error between observed and simulated runoff .The other catchments, like Manot, Mohegaon, and Kalu, exhibit 74, 73, and 68% efficiencies, respectively, in calibration, and 67, 37, and 84%, respectively, in validation. It follows that, except Hridaynagar, all other catchments are more amenable to the above model than the others showing lowest runoff coefficient or dry catchment. Notably, as expected, the efficiencies of all catchments, except Hemavati and Kalu, are higher in calibration than in validation, but reverse holds for the others. The average relative error (R.E.) values for Kalu and Hemavati are computed as 5 and 9%, respectively, and for Hridaynagar, Manot, and Mohegaon catchments are, respectively, 26, 19, and 17% (Table 4). These values generally exhibit a satisfactory model performance. The R.E.-values indicating negative values as seen in Table 4 imply that the model overestimates the runoff values. Figure 3a–e presents daily variations of estimated and observed runoff with respect to daily average rainfall for all the catchments. Also an average relative error is calculated for the study period and presented in Table 4. The relative error computed are 9, 25, 19, 17, and 5% in Hemavati, Hridaynagar, Manot, Mohegaon, and Kalu catchment, respectively.
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Table 4 Annual rainfall, observed runoff, simulated runoff and relative error (SCS-CN-based lumped model) Sl.No.
Year
Hemavati 1 1974 2 1975 3 1976 4 1977 5 1978 Average Hridaynagar 1 1981 2 1982 3 1983 4 1984 5 1985 6 1986 7 1987 8 1988 9 1989 Average Manot 1 1981 2 1982 3 1983 4 1984 5 1985 6 1986 7 1987 8 1988 9 1989 Average Mohegaon 1 1981 2 1982 3 1983 4 1984 5 1985 6 1986 7 1987 8 1988 9 1989 Average Kalu 1 1990 2 1991 3 1992 4 1993 Average
Rainfall (mm)
Observed Runoff (mm)
Simulated Runoff (mm)
Relative Error (%)
2,827 2,399 2,434 2,783 2,795 2,650
2,421 1,604 1,804 2,831 1,987 2,129
2,248 1,733 1,865 2,285 2,191 2,064
7 −8 −3 19 −10 9
1,433 1,400 1,755 1,179 1,187 1,632 834 1,370 1,025 1,313
308 306 364 287 237 584 173 557 280 344
248 284 411 268 210 361 99 269 172 258
19 7 −13 7 11 38 43 52 39 25
1,045 1,002 1,257 1,190 1,122 1,136 1,249 1,267 1,056 1,147
374 366 529 601 701 691 752 625 265 545
389 389 536 592 482 451 485 565 391 476
−4 −6 −1 2 31 35 36 10 −48 19
1,148 1,049 1,396 1,180 1,103 1,182 1,050 1,153 826 1,121
324 332 463 494 553 459 364 544 221 417
391 323 472 492 398 306 355 419 317 386
−21 3 −2 0 28 33 3 23 −43 17
3,348 3,169 1,903 3,129 2,887
3,528 3,058 1,601 2,944 2,783
3,339 3,194 1,477 2,974 2,746
5 −5 8 −1 5
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100
Hemavati 1974-1978 R.E.9%
120
200
Runoff (E) Runoff (O) Rainfall
80
300
40 0 1
400 71 141 211 281 351 421 491 561 631 701 771 841 911
a
Time in Days
Runoff in mm
75 Hridaynagar 1981-1990 R.E 25%
50
Runoff (E) Runoff (O) Rainfall
100
200 25
0
300 1
161 321 481 641 801 961 1121 1281 1441 1601
b
Time in Days 0
200
Runoff in mm
160
100 Manot 1981-1990 R.E.19%
120
Runoff (E) Runoff (O)
80
200
Rainfall
300
40 0
400 1
c
Rainfall in mm
0
100
161 321 481
641 801 961 1121 1281 1441 1601 Time in Days
Rainfall in mm
Runoff in mm
160
Rainfall in mm
0
200
SCS-CN-based continuous simulation model for hydrologic forecasting
100 Runoff (E)
120
Runoff (O)
80
Mohegaon 1981-1989 R.E.17%
200
Rainfall
300
40 0
400 1
161 321 481 641 801 961 1121 1281 1441 1601 Time in Days
d
0
500 400 Kalu-1990-1993 R.E.5%
300
Runoff (E) Runoff (O)
200
150
300
Rainfall
Rainfall in mm
Runoff in mm
160
Rainfall in mm
0
200
Runoff in mm
183
450
100 0
600 1
e
61 121 181 241 301 361 421 481 541 601 661 721 Time in Days
Fig. 3 (Continued)
As shown in Fig. 4a–b, the least deviation in RE-values is observed in Kalu catchment, i.e. 5% in 1990 in calibration, and 1% in 1993 in validation, respectively. The model performs satisfactorily in this catchment, except for few peaks, where the computed runoff is lower than the observed. It is largely because the limitation of the optimized function that is minimized based on a large number of other data points than the peaks. The sample
RFig. 3
a–e Daily variations of rainfall, observed runoff (O), estimated runoff (E) and average relative error (%) of different catchments for SCS-CN based lumped conceptual model
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0
250
50
Runoff (E) Runoff (O) Rainfall
150
150
100
200
50
Rainfall in mm
100
250
300
0 21
41
61
50
Kalu 1993 R.E. 0.98%
250 200
Runoff (E) Runoff (O)
150
250 300
50
350 400
0 1
21
41
61
81 101 121 141 161 181
Time in Days
b Validation
50
0
40
80
Runoff (E) Runoff (O) Rainfall
20
10
0 41
61
81 101 121 141 161 181
240
100
0
80
40
Hridaynagar 1988 R.E.51.66%
60
Runoff in mm
160
30
Rainfall in mm
Hridaynagar 1981 R.E.19.48% Runoff in mm
200
Rainfall
81 101 121 141 161 181
a Calibration
21
150
100
Time in Days
1
100
Runoff (E) Runoff (O) Rainfall
40
320
20
400
0
120
160
200 1
21
Time in Days
c Calibration
80
Rainfall in mm
1
0
350 300
Runoff in mm
Runoff in mm
200
400
Rainfall in mm
Kalu 1990 R.E.5.38%
41
61
81
101 121 141 161 181
Time in Days
d Validation
Fig. 4 a–d Calibration and validation of results of Kalu and Hridaynagar exhibiting the best and the worst performance respectively (SCS-CN-based lumped conceptual model)
calibration and validation results yielding the maximum errors on the data of Hridaynagar watershed is also presented in Fig. 4c–d, respectively. The resulting errors are 19% in 1981 in calibration, and 52% in validation in 1988, indicating a poor agreement between the observed and simulated runoff values. Also various component processes involved are also quantified and given in Table 5 & Fig. 5a–i. It is apparent that the initial abstraction takes a maximum value in Hridaynagar catchment, indicating less amount of rainfall reaching the ground to generate runoff. While comparing the runoff components, base flow is insignificant in all watersheds except Kalu catchment, whereas surface runoff is dormant only in Hridaynagar catchment. The runoff produced in the lateral direction (throughflow) is maximum in Hemavati
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Table 5 Percent estimates of hydrological components for different catchments (SCS-CN-based model) Sl. No.
Components
Hemavati (%)
Hridayanagar (%)
1 2 3 4 5 6 7 8 9 10 11 12
Rainfall (R) Initial abstraction (Ia) Effective Rainfall (Pe) Infiltration (Ft) Drainage (DRt) Percolation (PRt) Deep seepage (DPSt) Surface runoff (ROt) Throughflow (THRt) Base flow (BFt) Simulated runoff (TROt) Deep percolation (DPrt)
100 4.26 95.74 58.72 44.42 * 4.57 * 5.21 37.02 39.85 * 1.02 77.89 * 4.19
100 47.07 52.93 44.48 17.82 15.38 14.17 * 8.45 * 2.44 * 8.76 19.65 * 5.41
*
*
Manot (%)
Mohegaon (%)
Kalu (%)
100 0.52 99.48 78.55 35.48 24.67 51.44 20.92 10.81 * 9.76 41.49 41.67
100 42.11 57.89 45.76 25.33 * 5.35 * 5.17 12.13 19.98 * 2.32 34.43 * 2.85
100 17.70 82.3 35.33 35.05 28.06 55.59 46.97 * 7.00 41.15 95.12 14.44
*
Dormant process
catchment, and minimum in Hridaynagar catchment. Maximum amount of surface runoff is derived from the Kalu watershed for a given amount of rainfall, and least from the Hridaynagar catchment. Deep percolation is dormant in Hemavati, Hridaynagar, and Mohegaon catchments. Based on the results (Table 5), the dominancy/dormancy of the processes in each catchment can be identified.
7 Model Comparison This section compares the application of the two models, viz., the proposed SCS-CN-Based lumped conceptual model (Fig. 1) and the long term hydrologic VSA-Based model using storage and source area concepts (Mishra et al. 2005). Tables 6 and 7 compare the model efficiencies and average RE-values due to the above models. Both the models show a satisfactory performance (the higher model efficiencies and less relative error) on high runoff producing catchments, like Hemavati and Kalu catchments. The catchment in dry region, like Hridaynagar, indicates the lowest efficiency in both model applications. The comparison based on average relative error (Tables 6 and 7) indicates the model based on the modified SCS-CN technique performs significantly better than the model based on variable source area (VSA) theory. It is seen from Table 8 that Hridaynagar catchment generates monsoon runoff 26% of the average monsoon rainfall as observed whereas simulated ones are 20 and 19% derived from SCS-CN-based and VSA-based models, respectively. It implies that the losses are high in the Hridaynagar catchment. The other low runoff producing catchments, Manot and Mohegaon catchments, respectively produce 47 and 37% runoff. The runoff computed from SCS-CN-based and VSA-based models in Manot are 42 and 44%; and in Mohegaon are 34 and 30% of average monsoon rainfall. The wet catchments, viz., Kalu and Hemavati, produce high amount of runoff as observed and are, respectively 96 and 80% of average monsoon rainfall. The corresponding SCS-CN
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K. Geetha, et al. 50
60
45
Surface runoff
Throughflow
40 Throughflow (%)
Surface runoff (%)
50 40 30 20
35 30 25 20 15 10
10
5 0
0 1
2
3
a
4
5
6
7
8
1
9
4
5
6
7
8
9
Time in years
80
Base flow
40
70
35
60
Infiltration (%)
Base flow (%)
45
30 25 20 15
50 40 30
10
20
5
10
0
Infiltration
0 1
2
3
c
4
5
6
7
8
9
1
2
3
d
Time in Years
4
5
6
7
8
9
8
9
Time in Years 35
60 Drainage
Percolation
30 Percolation (%)
50 Drainage (%)
3
90
50
40 30 20 10
25 20 15 10 5
0
0 1
e
2
b
Time in years
2
3
4
5
6
Time in Years
7
8
1
9
f
2
3
4
5
6
7
Time in Years
Fig. 5 a–i Annual variations of various processes in runoff generation mechanism (SCS-CN-Based lumped conceptual model)
based model simulated values for these catchments are 95 and 78%, whereas the VSAbased model yields 81 and 71%, respectively, with respect to average monsoon rainfall. Thus, the results due to the SCS-CN-based model are closer to the observed than the VSA-based model, indicating the better performance of the proposed SCS-CN-based model in long-term hydrologic simulation.
SCS-CN-based continuous simulation model for hydrologic forecasting 120
50 45
Simulated runoff
100
40 Deep Percolation
Simulated runoff (%)
187
80 60 40
35 30
Deep Percolation
25 20 15 10
20
5 0
0 1
2
3
4
g
5
6
7
8
1
9
h
Time in Years
2
3
4
5
6
7
8
9
Time in Years
120
Hemavati Hridaynagar
Observed runoff Observed runoff(%)
100
Manot Mohegaon Kalu
80 60 40 20 0 1
2
3
i
4
5
6
7
8
9
Time in Years
Fig. 5 (Continued)
Table 6 Data length and model efficiency (%) with runoff coefficient Catchment
Area (km2)
Data Length (Monsoon Period)
Efficiency (%)
Calibration Validation SCS-CN-based (Years) (Years) lumped conceptual model
Runoff Coefficient VSA-based lumped conceptual model
Calibration Validation Calibration Validation Hemavati Hridaynagar Manot Mohegaon Kalu
600.00 3,370.00 5,032.00 4,661.00 224.11
− Negative efficiency
3 5 5 5 3
2 4 4 4 1
84.43 58.84 74.36 73.38 67.86
87.7 47.73 66.51 37.00 84.00
78.37 38.46 26.28 −13.93 69.70
68.92 46.05 55.60 51.86 78.18
0.803 0.262 0.475 0.372 0.964
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Table 7 Annual average rainfall, observed & simulated runoff and relative error (%) Sl. No.
1 2 3 4 5
Catchment
Hemavati Hridaynagar Manot Mohegaon Kalu
Average rainfall (mm)
2,650 1,313 1,147 1,121 2,887
Average observed runoff (mm)
2,129 344 545 417 2,783
Model based on SCS-CN concept
Model based on VSA concept
Average simulated runoff (mm)
Average Relative Error (%)
Average Simulated runoff (mm)
Average Relative Error (%)
2,064 258 476 386 2,746
9 26 19 17 5
1,875 243 501 339 2,332
12 29 22 19 16
8 Conclusion A new SCS-CN-based continuous simulation model for hydrologic forecasting has been presented here and applied to the data of watersheds of River Cauvery in Karnataka, River Narmada in Madhya Pradesh, and River Ulhas in Maharashtra in India. This model can be used in the computation of annual hydrologic components as well as total runoff values. When compared with the available VSA-based model, the SCS-CN-based model better represented the hydrologic behavior of the catchments with different soil, vegetation, and climate than did the VSA-based model. Both the models however performed satisfactorily on high runoff producing Kalu and Hemavati watersheds. Besides, initial abstraction was dormant in Hemavati and Manot catchments; deep percolation was negligible in Hemavati, Hridaynagar and Mohegaon catchments; base flow was insignificant in all catchments except Kalu watershed; the least runoff producing Hridaynagar catchment generated negligible surface runoff, throughflow, and base flow; the wet Kalu catchment produced relatively less throughflow compared to runoff; and percolation and deep seepage were dormant in Hemavati and Mohegaon catchments. As shown in this study, the present model can be used as a continuous simulation model for hydrologic forecasting of watersheds.
Table 8 Comparison of annual average runoff with respect to annual average rainfall Sl. No.
1 2 3 4 5
Catchment
Hemavati Hridaynagar Manot Mohegaon Kalu
Observed runoff (%)
80.36 26.21 47.49 37.20 96.43
Simulated runoff (%)
Category of watershed
Model based on SCS-CN concept
Model based on VSA concept
77.89 19.65 41.50 34.43 95.12
70.76 18.54 43.71 29.57 80.79
Wet Dry Normal Normal Wet
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