CHAPTER 2 LITERATURE REVIEW

Transcription

1 9 CHAPTER 2 LITERATURE REVIEW 2.1 INTRODUCTION The estimation of reservoir sedimentation has been the subject of several empirical studies since the 1950s. However, this prediction has never been an easy task due to complicated simultaneous processes involved such as sediment transport, erosion and deposition. Estimates of runoff and sediment yield are essential for the solution of a number of problems of a watershed such as drought, floods and soil erosion etc., Sediment is treated as a non-point source pollutant (Das 2000) due to which water quality management plans will have to include the control of sediment pollution from urban, agricultural, mining and construction activities. As a result, the modeling of rainfall-runoff and soil erosion / sediment yield from watersheds has received considerable attention. The models are expected to assist on the following aspects (Lorup and Styczen 1996, Haan et al 1982) Assessment of water yield, extent of soil and nutrient losses and sediment transport in various environments, Land use planning as they can provide important information on the effects of changes in land use and of implementation of different soil conservation measures, and

2 10 A better understanding of runoff and erosion processes and their interactions. These hydrologic modeling efforts have moved from traditional empirical lumped models to distributed models. In the recent past, attempts have been made to adopt computationally intensive and nonlinear but efficient techniques such as Neural Networks (NN) for rainfall-runoff (Hsu et al 1995) and soil erosion modelling (Licznar and Nearing 2003). Most of these models have adopted NN for temporal modeling of runoff with no emphasis on the spatial variations. (Dawson and Wilby, 1998, 2000) 2.2 CONVENTIONAL HYDROLOGICAL MODELS All models (conventional and modern) under real conditions are more or less incorrect (Lane et al 1988, Lorup and Styczen 1996), because of abstractions and simplifications of the actual complex and nonlinear hydrologic processes (Muftuoglu 1984). Moreover, model parameters are often impossible or difficult to directly measure, and thus, they are always data based rather than predetermined. About 28 hydrologic models, were evaluated by ASCE Task Committee (ASCE 1985), for adopting them in absence of calibration data for ungauged basins. The hydrologic modelling took off from traditional empirical models to conceptual lumped models and to the present day distributed models. The advent and availability of present day high speed and large memory computing systems have made the computationally intensive and inherently nonlinear techniques such as neural networks (NN) most applicable to hydrologic modelling Runoff Modelling In any hydrologic analysis, rainfall-runoff relationship plays (Das 2000, Haan et al 1982) the key role. There are a number of ways to

3 11 determine the amount of water that runs off of a surface. In addition to observations that are made in the field, we can also use computer models and simulations to estimate runoff. These are useful for extrapolating the observed runoff records by applying it on the historic rainfall data. Depending on the purpose, data availability, type of runoff result desired, the estimation is done (Ven Te Chow 1964, Haan et al 1982) by any of the methods, such as, i) linear and multiple linear regression models, ii) unit hydrograph method, iii) rational method and iv) hydrological models. Linear and multiple linear regression models (Haan et al 1982) relate rainfall (P) and runoff (R), using a statistical relation fitting a linear regression equation between R and P as, R=a.P + b (2.1) where, a is slope, b is intercept By incorporating some of the catchment characteristics of the watershed say drainage density (D d ), slope (S), land use (L u ) and soils, the multiple linear regression models can be formed as, R= a.p + b.d d + c.s + d.l u (2.2) where, a, b, c and d are regression coefficients. Runoff estimation in India (Das 2000) has also been carried out traditionally by using Barlow's tables, Strange's tables and Inglis formula. Empirical Models are developed (Chow 1964) using observed rainfall and runoff data and correlating with other basin parameters that are found to influence the runoff transformation process. Some of them are

4 12 Rational formula, Cook's table, SCS model, Dicken's formula, Ryves formula, Inglis formula, Craig's formula, Time of concentration, etc. Rational Method is based on (Haan et al 1982, Chow 1964) the catchment area, rainfall intensity and a runoff coefficient that depends on the land use, and is given as, q = C r i A (2.3) where, q is design peak runoff rate in cfs, C r, is runoff coefficient, i A is rainfall intensity in cm/hr uniformly occurring over the basin and over a period equal to or greater than time of concentration of the basin and is the watershed area in acres Cook's Method is a relationship (Haan et al 1982) between rainfall factor of a basin, return period of peak runoff and rainfall, which is given by, q = P.R.F (2.4) where, q is peak runoff, P R F is peak runoff from basin is geographic rainfall factor and is return period factor. Time of concentration approach uses the basin configuration in terms of basin length and the average basin slope and is given by, T c = L 0.77 /S (2.5) where, T c is Time of concentration in hours,

5 13 L S is length of the basin in miles, is average slope of basin Soil Conservation Service Runoff Curve Number (CN) method, is currently the most appropriate numerical model (Haan et al 1982, SCS 1985) in use by soil scientists. This method adopts the SCS runoff curve numbers that are based on the soil types and land use. This is a central loaded function as it assumes the average antecedent moisture conditions. Basin runoff in metric units is given by (P 0.2I) 2 a Q (P 0.8S) p (2.6) where, Q is direct surface runoff in inches, P is storm rainfall in inches, I a is initial abstraction (I a = 0.2 S p ), S p is maximum potential storage S p (2.7) CN where, CN is runoff curve number derived from combination of land use and hydrological soil group. (SCS 1985) Sediment Yield Modelling Numerical soil erosion prediction technology first became available 64 years ago. Publication of Zingg's equation (Zingg 1940) was followed by an extended period of research on fundamental processes (Bryan 2000, Cooley 1980) and the observation of soil erosion experimental plots on a wide

6 14 variety of site conditions including soils, slope, slope length, vegetation, agricultural practices, and climate (Ellison 1944, Musgrave 1947). The Agricultural Handbook No. 282, (Wischmeier and Smith 1965) documented the Universal Soil Loss Equation (USLE), an empirically based procedure to estimate annual soil erosion from croplands. Soil erosion is a spatially and temporally distributed (Kirby and Morgan 1980, Sharma and Singh 1995) process. The complex nature of soil erosion process makes its measurements difficult with field instruments. Recourse is taken to estimate erosion through prediction models (Laguna and Giraldez 1993, Meyer and Wischmeier 1969, Rose et al 1986a, 1986b) back computations from sediment yield or from field plot experiments. Some of the efforts made to develop methods to estimate soil erosion (Zingg 1940) are given below, (a) Total soil loss is related to the degree and length of slope as, X = c.s m l n (2.8) where, X is total soil loss from a land slope of unit width, s is degree of land slope, 1 is horizontal length of land slope, c m n is a constant of variation, is exponent of degree of land slope and is exponent of horizontal length of land slope, For practical purposes, the rational equation can be written as, X = c.s 1.4 l 1.6 (2.9)

7 15 (b) A soil loss equation was (Musgrave1947) developed based on factors such as soil erodibility, land cover, degree of slope, length of slope and 30-minute maximum rainfall with a frequency of 2 years. The soil loss formula is as follow, E = I R S 1.35 L 0.35 (P 30 ) 1.75 (2.10) where, E is soil loss (hectare-cm), I R is inherent erodibility of soil in cm, is cover factor, S is degree slope in %, L is length of slope in m, P 30 is maximum 30 min. amount of rainfall in cm, 2 year frequency (c) Sediment Yield Prediction Equation (SYPE) was developed (Flaxman 1974) for soil erosion assessment including terrain factors such as topography, soils, rainfall, the climatic factors. The model is given as below, log(y+100) = log(x ) log(x ) log (X ) log(x ) (2.11) where, Y is Sediment yield acre-feet / mi 2 /yr,

8 Y= X X X X 4 (2.12) 16 X 1 is ratio of average annual precipitation in inch X 2 is average watershed slope in %, X 3 is % of soil particles coarser than 1mm in the surface of 2 inch soil profile, and X 4 is % of soil particles of clay size 2 micron or finer in size. This above equation was modified (Flaxman 1974) into a multiple linear regression form, which is given by, Remotely sensed data was used to include land use of the study area and integrating it in a GIS, to estimate distributed soil loss by SYPE and USLE and a comparison of the results was made (Srinivasulu 1994) with observed value of soil loss. Studies to assess soil erosion was made using USLE approach (Nissar 1999), in which the FUZZY membership approach in a GIS context for classifying the erosion susceptibility was used. (d) Yet another regression based sediment yield model was developed using data from 50 catchments in India (Garde and Kothyari 1986) that includes the physiographic factors such as slope, drainage density, soils, land use and climatic factor i.e. rainfall, given by, P Y = 0.2F 1.7 e S D d P max 0.19 P m a (2.12) where, Y is annual sediment yield in cm, D d is drainage density,

9 17 S P a is the land slope, is annual rainfall in cm, P max is average maximum monthly rainfall in cm, F e is erosion factor defined as, 1 F e = A i (0.8A A +0.6A g + 0.3A F + 0.1A W ) (2.14) where, A A is arable area in km 2, A g is grass and scrub area in km 2, A w is waste area in km 2, A F is protected forest area in km 2, A i is arbitrary coefficient. Assessment of sediment yield for upper Indravati river basin is carried out using the model developed by (Garde and Kothyari 1987) for varying land uses (Kulkarni et al 1995, CWPRS 1994) for 16 years of mining project activities. (e) Universal soil loss equation (USLE) Method was developed by the USDA Agricultural Research Station (ARS) (Wischmeier and Smith 1978), including all the relevant terrain and climatic attributes like topography, soil types, rainfall and cultural activities, USLE method is given by, A = R K L S C P (2.15) where, A is average annual soil loss in tones / ha, R is rainfall factor given by, EI / 100, K is soil erodibility factor,

10 18 L is slope length in m, S C P (f) is steepness factor, is cropping & management factor, 1 for good cropping and others <1 and is supporting conservation practice (terracing, strip cropping &contouring). Williams (1975), modified the USLE to Modified Universal Soil Loss Equation MUSLE by replacing the rainfall factor with runoff factor, as A = B (V Q Q P ) 0.56 K L S C P (2.16) Where, V Q is the volume of runoff in acre-feet, Q P is peak flow rate in cfs, and B is constant Texas Agricultural Experiment Station simulated (Arnold et al 1990) hydrology, sedimentation, and nutrient and pesticide transport in large, complex rural watersheds using a model called the Simulator for Water Resources in Rural Basins Water Quality (SWRRBWQ). The model operates on a continuous daily time scale and allows for subdivision of basins to account for differences in soils, land use and rainfall. SWRRBWQ includes five major components: weather, hydrology, sedimentation, nutrients and pesticide. Soil and Water Assessment Tool-SWAT model was used (Arnold et al 1995) to predict the effect of management decisions on water, sediment, nutrient and pesticide yields with reasonable accuracy on large, ungauged river basins on a daily time step. The components of the model are weather,

11 19 surface runoff, return flow, percolation evapotranspiration, transmission losses, pond and reservoir storage, crop growth and irrigation, groundwater flow, reach routing, nutrient and pesticide loading water transfer. 2.3 ARTIFICIAL NEURAL NETWORKS In recent years, the artificial neural network (ANN) technique has shown excellent performance in regression, especially when used for pattern recognition and function estimation (ASCE Task Committee on Application of the Artificial Neural Networks in Hydrology 2000a,b) It is a highly nonlinear tool that can capture complex interactions among the input and output variables without any prior knowledge about the nature of these interactions (Hammerstrom 1993). In comparison to conventional methods, ANNs can tolerate imprecise or incomplete data, approximate information, and presence of outliers and are well suited to this problem (Haykin 1999). A numerical model, Hydrological Simulation Program Fortran (HSPF), developed by the U.S. Environmental Protection Agency was adopted to simulate the sediment yield. However, the calibration/verification of such a model was found to be complicated and tedious. Therefore, an ANN model was used to estimate the sediment yield. The synthetic sediment yield data generated from HSPF model was used to develop a BP ANN model due to scarcity of measurements. The rainfall intensity and discharge were considered as input parameters and sediment yield was treated as an output parameter. The transfer function generally used in hydrologic modelling (Dawson and Wilby 1998, 2000) is the sigmoid function, which allows

12 20 network to consider nonlinear relationship between input and output. Sigmoid function is given by, 2 f (h) = 1 h 1 e (2.17) where, h is input to the node, f (h) is the node output, and is gain introduced to consider non linear behavior of input data. The training process consists in determining a new set of weights that minimises the mean squared error E, of the output given by, 1 2 E (t O (2.18) k1 k k ) where, t k is the desired output at node k, O k is the network output. As the transfer function is non-linear, the error E will be non-linear function of the weights w. The weights are adjusted using generally a steepest descent approach as E E w (w) old (2.19) w w ji where, is learning rate, is the fraction of average change in weights, (w) old is momentum term, and and are take between 0 and 1

13 21 Consequently, the combined signal from input through hidden layer to the output layer is modified by the so called transfer function to produce the output signal (Berthold and Hand 1999, ASCE 2000a,b) as, O k n w n m f ( h ) w f wli ri (2.20) ki j kj j1 j1 l1 where, f denotes the selected transfer function, l is total number of nodes in output layer, and w is weight assigned to path j to k Runoff Modelling A neural network was adopted for flood forecasting (Kim and Barros 2001) with four nodes in the input layer, (P 1, P 2, P 3, P4) three nodes in the hidden layer (H 1, H 2, H 3 ) and one node in the output layer (Q) as shown in Figure.2.1. The rainfall data from four raingauge stations were fed as input at input nodes. Besides the four predictor rain gauge data, current stream flow was also used as input to NN model. Figure2.1 Artificial Neural Network for Rainfall-Runoff

14 22 Hsu et al (1995) demonstrated the use of ANN as a rainfall-runoff model. They proposed a linear least square simplex algorithm to train a threelayer feed-forward network and demonstrated the potential of such models for simulating the hydrologic behavior of a watershed, they also showed that the ANN model approach provides a better representation of the rainfall -runoff relationship of a medium-sized basin than the ARMA model. Dawson and Wilby (1998) applied an ANN for river flow forecasting and highlighted their ability to cope with missing data and learn from the event currently being forecast in real time. It was also found that an ANN was the most efficient of the black box models (Luk et al 1998, Sajikumar and Thandaveswara 1999, Shamseldin 1997, Wu et al 2005) that were tested for calibration of short period rainfallrunoff models. Improvements in the runoff were found (Rajurkar et al 2003) to result by inclusion of past runoff along with past and present rainfalls. Application of ANN for river flow forecasting was studied (Thirumallaiah and Deo 1998, 2000) in which a three layer neural network was found suitable for prediction of river stages. Application of ANN for forecasting of inflows and reservoir operation was studied. (Jain et al 1999) Sediment Yield Modelling Applicability of neural networks to quantitatively predict soil erosion from plot scales was attempted (Licznar and Nearing 2003) using eight parameters in which it was noticed that the type of transfer function and the number of neurons in the network did not make appreciable changes in the quality of results.

15 23 There are numerous studies related to the application of ANNs to various problems frequently encountered in water resources. The nonlinear ANN approach was shown to provide a good representation of the rainfallrunoff relationship (Hsu et al 1995, Minns and Hall 1996). The application of the radial basis function type of ANNs to model the rainfall runoff process has also been examined (Fernando and Jayawardena 1998). Tokar and Johnson (1999) employed neural network methodology to forecast daily runoff as a function of daily precipitation, temperature, and snowmelt for the Little Patuxent River in Maryland. Campolo et al.(1999) used ANNs to forecast river flows during heavy rainfall and low-flow periods. ANNs were also considered to be a powerful tool for use in various groundwater problems Rogers and Dowla (1994). While ANNs have been popular choices for sediment transport models, only limited studies have so far been conducted for estimating reservoir sedimentation using ANNs. Abrahart and White (2001) carried out some initial experiments to assess the competence of a backpropagation (BP) network to produce a combined model of sediment transfer occurring under different types of agriculture and land management conservation regimes. Cigizoglu (2002a) used ANNs to forecast and estimate sediment concentration values. Similarly, Cigizoglu (2002b) compared ANN and sediment rating curves for two rivers with very similar catchment areas and

16 24 characteristics in north England. Cigizoglu (2004) forecasted daily suspended sediment load in a stream by multilayer perceptrons (MLPs). According to Licznar and Nearing (2003), neural networks may provide a user-friendly alternative to complex, physically based models for soil erosion prediction. Sarangi and Bhattacharya (2005) compared the performance of ANN models for sediment loss prediction with a regression model for the Banha watershed in India. Two ANN models-one geomorphology-based and the other non-geomorphology-based were developed for predicting sediment yield and validated using the hydrographs and silt load data of Sarangi et al (2005) developed ANN and regression models using watershed geomorphologic parameters to predict surface runoff and sediment loss of the St. Esprit Watershed, Quebec, Canada. Agarwal et al (2005) developed feed-forward error BP ANN and linear-transfer-function-sediment-yield models for the Vamsadhara River basin. Cigizoglu and Alp (2006) developed an ANN model for river sediment yield using a generalized regression algorithm. Such generalized regression neural networks do not require an iterative training procedure as in the conventional BP method. ANN models were developed by Raghuwanshi et al (2006) to predict both runoff and sediment yield on a daily and weekly basis, for Upper Siwane River watershed, India. A total of five models were developed by the authors for predicting runoff and sediment yield, of which three models were based on a daily interval and the other two were based on a weekly interval.

17 25 Lee et al (2006) conducted quantitative estimation of reservoir sedimentation for Shihmen Reservoir watershed in Taiwan. Temporal variations of water surface elevation, discharge, and concentration of suspended sediment were measured during three typhoon events in the field. 2.4 SUMMARY The conventional, conceptual / physical models developed (Sugawara 1978) and used earlier in hydrological applications involved tremendous efforts in collecting and processing of data on many parameters and the analysis of the results. Though these models are complex and rigorous mathematical formulations, they involve subsequent numerical approximations and simplifications for the convergence of the solutions desired. It may be observed from the above that, i) the traditional models are complex and do not incorporate the spatial variability of the parameters, ii) though, ANNs for their excellent merits are best suited for hydrological modelling they have been applied mostly on a temporal basis Therefore, in the present studies, an attempt is made to develop approaches to overcome most of these problems and incorporate multiple variables and their temporal and spatial variability in Runoff modelling and Sediment yield modelling. In the present study, analytical method proposed by Garde et al (1978) is first used to calculate the volume of sediment deposited (V ac ) in the Vaigai reservoir for the available annual inflow data ( ) and also for future years. The available annual inflows are used to generate the flows for the subsequent years using Brittan s method by means of Markov-Chain model as given by Ven Te Chow (1964).

18 26 Secondly, an ANN model has been developed using available measurements for estimation of the volume of sediment in the Vaigai reservoir in India. The input parameters such as annual rainfall, annual inflow and capacity in that particular year were decided on basis of their influence in the sedimentation process and sediment deposition was an output parameter. 2.5 OBJECTIVES Due to the complexity in the conventional method of runoff estimation and sediment yield calculation, ANN has been employed recently in runoff and sediment yield modelling for better results. In view of the above, the specific objectives of the present studies are, 1. To predict the volume of sediment deposition in Vaigai reservoir using analytical approach 2. To compute the capacity of reservoir by analytical and ANN approach 3. To develop ANN model for rainfall-runoff relationship 4. To develop ANN model for predicting the sediment yield in the reservoir. 5. To compare the models and suggest the best one for sedimentation problems.