SWAT MODEL AND SWAT-CUP SOFTWARE

Transcription

1 CHAPTER 4 SWAT MODEL AND SWAT-CUP SOFTWARE 4.1 SWAT model hstory SWAT s the acronym for Sol and Water Assessment Tool. It s a rver basn, or watershed scale model developed by Jeff Arnold for the USDA Agrcultural Research Servce (ARS). SWAT was developed to predct the mpact of land management practces on water, sedment and agrcultural chemcal yelds n large complex watersheds wth varyng sols, land use and management condtons over long perods of tme. SWAT s a physcally based model whch requres specfc nformaton about weather, sol propertes, topography, vegetaton and land management practces occurrng n the watershed. The physcal processes assocated wth water movement, sedment movement, crop growth, nutrent cyclng, etc. are drectly modelled by SWAT usng ths nput data. SWAT can be used for modellng ungauged watersheds. The relatve mpact of alternatve nput data - changes n management practces, clmate, vegetaton etc. - on water qualty or other varables 74

2 of nterest can be quantfed usng SWAT model. Smulaton of very large basns or a varety of management strateges can be performed wthout excessve nvestment of tme or money. Ths s a contnuous tme model and s not desgned to smulate detaled sngle-event flood routng. The SWAT model s a drect outgrowth of the SWRRB model (Smulator for Water Resources n Rural Basns) whch s a contnuous tme step model that was developed to smulate non-pont source loadngs from watersheds (Wllams et al., 1985; Arnold et al., 1990). Specfc models that contrbuted sgnfcantly to the development of SWAT were CREAMS (Chemcals, Runoff, and Eroson from Agrcultural Management Systems) (Knsel, 1980), GLEAMS (Groundwater Loadng Effects on Agrcultural Management Systems) (Leonard et al., 1987), and EPIC (Eroson-Productvty Impact Calculator)(Wllams et al., 1984). The SWAT model was developed n the early 1990s and t has undergone many changes and mprovements snce ts ntal formulaton. Hydrologc processes smulated by the model nclude precptaton, nfltraton, surface runoff, evapotranspraton, lateral flow and percolaton. The model uses a command structure smlar to the structure of the Hydrologc Model (HYMO) (Wllams and Hann, 1978) for routng runoff and chemcals through a watershed. Commands are ncluded for routng flows through streams and reservors, addng flows, and usng measured data for pont sources. Interfaces for the model have been developed n Wndows (Vsual Basc), GRASS, MapWndow, ArcVew and ArcGIS. SWAT wth ArcVew platform s the AVSWAT for SWAT2000 verson and AVSWAT-X for SWAT2005 verson. SWAT wth ArcGIS platform s the ArcSWAT. SWAT model has undergone extensve valdaton. 4.2 Overvew of SWAT SWAT allows a number of dfferent physcal processes to be smulated n a watershed. For modellng purposes, a watershed may be parttoned nto a number of sub watersheds or sub basns. The use of sub basns n a smulaton s partcularly benefcal when dfferent areas of the watershed are domnated by land uses or sols 75

3 dssmlar enough n propertes to mpact hydrology. By parttonng the watershed nto sub basns, the user s able to reference dfferent areas of the watershed to one another spatally. Input nformaton for each sub basn s grouped or organzed nto the followng categores: clmate; hydrologc response unts or HRUs; ponds/wetlands; groundwater; and the man channel, or reach, dranng the sub basn. Hydrologc response unts are lumped land areas wthn the sub basn that comprse of unque land cover, sol and management combnatons. No matter what type of problem s studed wth SWAT, water balance s the drvng force behnd everythng that happens n the watershed. To accurately predct the movement of pestcdes, sedments or nutrents, the hydrologc cycle as smulated by the model must conform to what s happenng n the watershed. Smulaton of the hydrology of a watershed can be separated nto two major dvsons. The frst dvson s the land phase of the hydrologc cycle, depcted n Fgure 4.1. The land phase of the hydrologc cycle controls the amount of water, sedment, nutrent and pestcde loadngs to the man channel n each sub basn. The second dvson s the water or routng phase of the hydrologc cycle whch can be defned as the movement of water, sedments, etc. through the channel network of the watershed to the outlet. SWAT smulaton s based on the water balance equaton : SW t = SW o + t (R day Q surf E a w seep Q gw ) (4.1) =1 where, SW t = sol water content at tme t, SW o = ntal sol water content, t = tme (n days), R day = amount of precptaton on day, 76

4 Fgure 4.1: Schematc representaton of the hydrologc cycle Q surf = amount of surface runoff on day, E a = amount of evapotranspraton on day, w seep = water percolaton to the bottom of the sol profle on day, and Q gw = amount of water returnng to the ground water on day. For the estmaton of surface runoff, the SCS curve number (CN) s used n the model. Ths method uses two equatons for runoff computaton. The frst relates runoff to ranfall, and retenton parameter s gven as : 77

5 where, Q = (R 0.2S)2, R > 0.2S (4.2) R+0.2S Q = daly surface runoff (n mm), R = daly ranfall (n mm), S = retenton parameter, the maxmum potental dfference between ranfall and runoff (n mm) startng at the tme the storm begns. The second equaton relates retenton parameter to curve number as : ( ) 1000 S = 25.4 CN 10 (4.3) where CN = curve number rangng from 0 CN 100. The SCS curve number depends on the nfltraton characterstcs of the sol, landuse and the antecedent sol mosture condton. The SCS defnes three antecedent sol mosture condtons : I - dry (wltng pont), II - average most, and III - wet. The mosture condton I curve number s the lowest value that the daly curve number can assume n dry condtons. The standard values of curve number shown n SCS tables for varous land cover and sols are based on antecedent sol mosture condton II. The standard values for curve number can be adjusted for drer or wetter antecedent condtons usng the followng equatons : CN 1 = CN 2 20 (100 CN 2 ) (100 CN 2 +exp[ (100 CN 2 )]) (4.4) CN 3 = CN 2 +exp[ (100 CN 2 )] (4.5) where, CN 1 = mosture condton I curve number, 78

6 CN 2 = mosture condton II curve number, CN 3 = mosture condton II curve number. SWAT uses typcal curve numbers for varous sols wth mosture condton II and a set slope of 5 percent. To adjust the curve number to dfferent slopes, an equaton developed by Wllam (1995) was used, as gven by Equaton 4.6 : CN 2s = CN 2 CN 3 3 [1 2 exp( 13.6 slp)]+cn 2 (4.6) where, CN 2s = mosture condton II curve number adjusted for the slope, CN 3 = mosture condton III curve number for default 5 percent slope, CN 2 = mosture condton II curve number for default 5 percent slope, slp = average percent slope of the sub-watershed. Fgure 4.2 shows the general sequence of processes used by SWAT to model the land phase of the hydrologc cycle. The subdvson of the watershed enables the model to reflect dfferences n evapotranspraton for varous crops and sols. Runoff s predcted separately for each HRU and routed to obtan the total runoff for the watershed. Ths ncreases accuracy and gves a much better physcal descrpton of the water balance Hydrology As precptaton descends, t may be ntercepted and held n the vegetaton canopy or fall to the sol surface. Water on the sol surface wll nfltrate nto the sol profle or flow overland as runoff. Runoff moves relatvely quckly toward a stream channel and contrbutes to short-term stream response. Infltrated water may be held n the sol and later evapotranspred or t may slowly make ts way to 79

7 Fgure 4.2: HRU/sub basn command loop 80

8 the surface water system va underground paths. The potental pathways of water movement smulated by SWAT n the HRU are llustrated n Fgure 4.3. Fgure 4.3: Schematcs of pathways avalable for water movement n SWAT Routng phase of the hydrologc cycle Once SWAT determnes the loadngs of water, sedment, nutrents and pestcdes to the man channel, the loadngs are routed through the stream network of the watershed usng a command structure smlar to that of HYMO (Wllams and Hann, 1972). In addton to keepng track of mass flow n the channel, SWAT models the transformaton of chemcals n the stream and stream-bed. Flow s routed through the channel usng a varable storage coeffcent method developed by Wllams (1969) or the Muskngum routng method. 81

9 4.3 SWAT-CUP5 software SWAT Calbraton and Uncertanty Program (SWAT-CUP) s a computer program whch provdes the calbraton, valdaton and senstvty analyss of SWAT models. It nvolves several methods such as SUFI2, PSO, GLUE, ParaSol, and MCMC whch can be chosen for the purpose of calbraton and uncertanty analyss. Ths accesses the SWAT nput fles and runs the SWAT smulatons by modfyng the gven parameters. The storage of the value of the objectve functon and the modfcaton of parameters are the bass for comparson Objectve functon SWAT-CUP provdes a set of several objectve functons. A multplcatve form of the square error s gven by g = (Q m Q s ) 2 n Q (S m S s ) 2 n S (N m N s ) 2... (4.7) n N The summed square error s gven by n 1 n 2 n 3 g = w 1 (Q m Q s ) 2 +w 2 (S m S s ) 2 +w 3 (N m N s ) (4.8) The coeffcent of determnaton s gven by 82

10 R 2 = [ T ] (Q t m Q m )(Q t o Q o ) 2 (4.9) T (Q t m Q m ) 2 T (Q t o Q o ) 2 Ch-squared s gven by χ 2 = T (Q t m Q o ) 2 σ 2 Q (4.10) The Nash-Sutclffe coeffcent s gven by N SE = 1 T (Q t o Q t m) 2 (4.11) T (Q t o Q 0 ) 2 where, N SE = Nash-Sutclffe coeffcent, Q o = observed dscharge, Q m = modeled dscharge, Q o = mean observed dscharge, Q t = dscharge at tme t. 83

11 The two coeffcents of the regresson lne multpled wth the coeffcent of determnaton, R 2 are gven by b R 2 f b 1 φ = b 1 R 2 f b > 1 (4.12) and fnally the SSQR method s gven as SSQR = T (Q t o Q t m) 2 (4.13) The Nash-Sutclffe effcency approach s the most commonly used method for hydrologcal applcatons. Goodness of ft can be quantfed by the R 2 and/or Nash-Sutclffe (N SE ) coeffcent between the observatons and the fnal best smulaton. It should be noted that the best smulaton, as n a stochastc procedure, s not taken but the best soluton s actually the fnal parameter ranges. Automated model calbraton requres that the uncertan model parameters are systematcally changed, the model s run, and the requred outputs (correspondng to measured data) are extracted from the model output fles. The man functon of an nterface s to provde a lnk between the nput/output of a calbraton program and the model. The smplest way of handlng the fle exchange s through text fle formats. SWAT-CUP s an nterface that was developed for SWAT. Usng ths generc nterface, any calbraton/uncertanty or senstvty program can easly be lnked to SWAT. SWAT model s run n ArcGIS nterface. For calbratng a model 84

12 run, open a new project n SWAT-CUP and locate the TxtInOut drectory of the SWAT run. Choose the program from the lst provded (SUFI2, GLUE, ParaSol, MCMC, PSO). After completng the nputs requred, the program s executed. The output fles for the best parameters and the best smulaton are taken as the results. Among the methods offered wthn the SWAT-CUP package, the SUFI2 algorthm was adopted n ths study for the calbraton purpose, snce t s easy to handle; t requres a mnmum of runs and thus gves comparably good results. Moreover, t s able to descrbe all knds of uncertanty sources. 4.4 Model performance crtera Characterstcs of a good model The performance of a model s evaluated by means of several characterstcs. Three mportant charcterstcs of a good model (Kachroo, 1992) are : 1. Accuracy 2. Consstency 3. Versatlty The term (model) accuracy s used for representng the satsfactory level of attanment of the prncpal objectve. On the other hand, (model) consstency s used for representng the characterstcs of the model whereby the level of accuracy and the estmate of the parameter values persst through dfferent samples of the data. A versatle model s defned as the model whch s accurate and consstent when used for the dverse nvolvng model evaluaton crtera not drectly based on the objectve functon used durng the calbraton of the model (Kachroo, 1992). 85

13 4.4.2 Model evaluaton crtera A varety of verfcaton crtera whch could be used for the evaluaton and nter-comparson of dfferent models were proposed by the World Meteorologcal Organsaton (WMO) and other nvestgators (World Meteorologcal Organsaton, 1975; Nash and Sutclffe, 1970; Atken, 1973; Kachroo, 1992). They are grouped as the graphcal and the numercal performance ndcators. Graphcal ndcators WMO has lsted four ndcators as graphcal, out of whch, ndcators suted to the present objectve are chosen as lsted below: 1. A lnear scale plot of the smulated and observed hydrograph for both calbraton and valdaton perod. 2. A double mass plot of the smulated versus observed dscharge volumes for valdaton perod. 3. A scatter plot of the smulated and observed dscharges for the valdaton perod. Numercal ndcators Out of the several numercal ndcators, sutable ones for the present study are chosen. To check the predctve capablty of SWAT model, Santh et al. (2001) and Coffey et al. (2004) recommended the use of the correlaton coeffcent (R 2 ) together wth the Nash-Sutclffe model effcency coeffcent (N SE ) (Nash and Sutclffe,1970)asamethodtoevaluateandanalysesmulatedmonthlydata. TheR 2 (Equaton 4.9) value s a measure of the strength of the lnear correlaton between the predcted and observed values. The N SE value, whch s a measure of the predctve power of the model, s defned by Equaton A value of 1 for N SE ndcates 86

14 a perfect match between smulated and observed data values. A value of 1 for the R 2 also ndcates a perfect lnear correlaton between smulated and observed data values. In order to avod certan problems assocated wth R 2, an ndex of agreement (d) (Wllmott, 1981), s presented (Equaton 4.14). Ths statstc reflects the degree to whch the observed varable s accurately estmated by the predcted varable; d s not a measure of correlaton n the formal sense but rather a measure of the degree to whch a model s predctons are error free. It vares between 0 (complete dsagreement between predcted and observed values) and 1 (perfect agreement). It s a dmensonless statstcs and ts value should be evaluated based on (a) the phenomenon studed, (b) measurement accuracy, and (c) the model employed. The ndex of agreement s gven by d = 1 T (Q t m Q t o) 2 (4.14) T ( Q t m Q t 0 + Q t m Q t 0 ) 2 Moras et al. (2007) suggested a general performance ratng for the recommended statstcs for a monthly tme step (Table 4.1) for SWAT model. Table 4.1: Performance ratngs of recommended statstcs for monthly streamflow Performance ratng RSR N SE PBIAS (%) Very good 0.00 RSR < N SE 1.00 PBIAS < 10 Good 0.50 < RSR < N SE PBIAS < 15 Satsfactory 0.60 < RSR < N SE PBIAS < 25 Unsatsfactory RSR > 0.70 N SE 0.50 PBIAS 25 87

15 In Table 4.1, the N SE gven s the Nash-Sutclffe model effcency coeffcent computed usng Equaton The RSR s the root mean square error (RM SE) - observatons standard devaton rato; the RSR s calculated as the rato of the RMSE and standard devaton of measured data, as gven by RSR = RMSE STDEV obs = [ n [ n (Y obs =1 ] Y sm ) 2 ] (4.15) (Y obs Y mean ) 2 =1 Percent bas (P BIAS) measures the average tendency of the smulated data to be larger or smaller than ther observed counterparts. The optmal value of P BIAS s 0.0, wth low-magntude values ndcatng accurate model smulaton. Postve values ndcate model underestmaton bas, and negatve values ndcate model overestmaton bas. P BIAS s calculated usng the equaton: [ n =1 PBIAS = (Y obs Y sm n (Y obs ) =1 ) (100)] (4.16) where P BIAS s the devaton of data beng evaluated, expressed as a percentage; Y obs s the observed value; Y sm observed values. s the smulated value and Y mean s the mean of 88