Advertisement

Applied Water Science

, 8:147 | Cite as

Simulation of surface runoff in Karaj dam basin, Iran

  • Maryam Alipour
  • Majid HosseiniEmail author
Open Access
Original Article
  • 892 Downloads

Abstract

A major part of the Iranian capital drinking water is supplied from Karaj reservoir 100 km northwest of Tehran. This reservoir collects water from 849 km2-catchment which is undergoing accelerated changes due to deforestation and urbanization. The main objective of this study is to develop a catchment modeling platform which translates ongoing land-use changes, soil data, precipitation and evaporation into surface runoff of the river discharging into the reservoir: Soil and Water Assessment Tool, SWAT, model along with hydro-meteorological records of 1997–2011. A variety of statistical indices were used to evaluate the simulation results for both calibration and validation periods; among them, the robust Nash–Sutcliffe coefficients were found to be 0.58 and 0.62 in the calibration and validation periods, respectively. This project has developed a reliable modeling platform with the benchmark land physical conditions of the Karaj dam basin.

Keywords

SWAT model Land-use change Rainfall-runoff modeling Sensitivity analysis SWAT-CUP SUFI-2 

Introduction

More detail information on the status of rainfall runoff increases decision-making power on future programs for watershed managers of natural resources for sustainable development. Recently, rainfall-runoff models are widely used by hydrologists to simulate watersheds runoff and play a key role in water resources management (Bilondi et al. 2013). On the other hand, several programs and techniques have been developed to reduce uncertainty of parameters to get best fit of parameters in the hydrological modeling (Singh and Dutta 2016). These hydrological models are utilized in assessing the land-use change, effects of climate change, water quantity and quality of river flow, forecasting of flood, development and protection of water resources, integrated management of water components and most of the water resource management measures (Singh and Woolhiser 2002, 2016; Wurbs 1998). In most of the basins that require water resource planning, there is a lack of hydrometric stations for surface runoff measurement, or the data of measurement stations are incomplete; it is also unlikely to have measurement stations in all regions in the near future. Hydrological models, therefore, can evaluate the obtained runoff from precipitation in basins with no or incomplete surface runoff data with minimum cost and time by simulating the rainfall-runoff process.

Soil and Water Assessment Tools (SWAT) is one of the most popular hydrological models for runoff simulation in the world. The SWAT model is a continuous and distributed hydrological model to perform at the watershed scale (Hosseini and Ashraf 2015; Krysanova and Srinivasan 2015). This model introduced by Arnold et al. (1998) and further developed by Arnold and Fohrer (2005) can simulate hydrological processes in basins of small, medium and large scales. This model can be run on monthly, daily and hourly time scales and can simulate all components of water balance (Arnold et al. 1998).

Several studies have been conducted on different basins using SWAT model. This model has been widely used to land-use change effect assessment (Shen et al. 2010; De Girolamo and Lo Porto 2012; Yang et al. 2012; Du et al. 2013; Huang et al. 2013; Niu and Sivakumar 2014), sediment prediction (Shen et al. 2010), climate change (Andersson et al. 2006; Zhang et al. 2012; Huang et al. 2015), water quality (Debele et al. 2008; Zhang et al. 2011) and simulation of evapotranspiration (Wang et al. 2006). Many computer programs have been developed by hydrologists for parameters uncertainty analysis in river basin model, such as, generalized likelihood uncertainty estimation (GLUE; Beven and Binley 1992), sequential uncertainty fitting (SUFI-2; Abbaspour et al. 2004), parameter solution (ParaSol; Van Griensven and Meixner 2006) and Markov chain Monte Carlo (MCMC; Kuczera and Parent 1998; Vrugt et al. 2008).

SWAT model was selected among fifteen hydrological models to separate water components, which successfully fulfilled the annual and monthly water budget estimation as well as suspended sediment yield in the Taleghan catchment during 1987–2007, with a reliable accuracy (Hosseini 2010). The SWAT-CUP (Abbaspour et al. 2007b) is a computer program that links the sequential uncertainty fitting (SUFI-2) algorithm to SWAT model. Up to now, researchers used SUFI-2 algorithm for model calibration and uncertainty analysis of parameters of SWAT model as a popular method in the world. Fukunaga et al. (2015) investigated the application of the SWAT hydrologic model in a tropical watershed at Brazil. Narsimlu et al. (2015) in the Kunwari river basin applied SUFI-2 algorithm for model calibration, sensitivity and uncertainty analysis for 19 years from 1987 to 2005. Nyeko (2015) assessed the capabilities and limits of SWAT model in modeling watershed that has limited field and hydrologic data for possible use in water resources management. Krysanova and Srinivasan (2015) assessed five projects of different applications of SWAT covering the impacts of land cover change, climate change and combined impacts of climate change and human activities in water management. Vilaysanea et al. (2015) applied SWAT model to test the capability of the model for predicting stream flow and used SUFI-2 algorithm for calibration and uncertainty analysis in Xedone river basin. Lin et al. (2015) in their study found out the effects of land-use and land cover changes on runoff response using SWAT model. They used two different land-use scenarios (1985 and 2006, with reduced forest and increased cropland and urbanized area) in Jinjiang catchment. Hosseini et al. (2012) studied the effects of land-use changes on the water balance components in Taleghan basin using SWAT model. Both SUFI-2 and Parasol programs were used to calibrate and verify the model using the data from the period 1987–2007. A comparison of the water budget components performed by this model in Taleghan catchment from 1995 to 2004 using the three land-use maps of 1987, 2001 and 2007 by Hosseini and Ashraf (2015) showed reliable results for both calibration and validation periods.

The main objective of this study is to perform possibility of the SWAT model and SUFI-2 algorithm for runoff simulation of the study area, which will contribute to the reservation of natural resources in the Karaj dam basin.

Materials and methods

Study area

Karaj dam basin is located in the southern slopes of the Mountain Alborz between the geographical coordinate’s longitude 51°E–51°21′E and latitudes 35°30′N–36°6′N (Fig. 1). The area of this basin is 84,894 hectares and average annual water flow for Karaj Dam is 472 million cubic meters. The average daily discharge is estimated 154.54 m3 s−1. There are ten hydrometry stations to measure discharge in the study area. The Sira hydrometry station, located above the Karaj dam, selected to runoff simulation.
Fig. 1

Location of Karaj dam watershed

Description the SWAT model

Soil and Water Assessment Tools (SWAT) is a physically based model for assessing the impact of management and climate on water supplies, sediment and agricultural chemical yields in catchments (Narsimlu et al. 2015). This model is a basin-scale, spatially distributed watershed delivery model developed by the Agricultural Research Service (ARS) at the US Department of Agriculture (USDA). Its purpose is to simulate water, sediment and chemical yields on large river basins and possible impacts of land use, climate changes and watershed management. Output from SWAT can be daily, monthly or yearly, but in all cases is based on a daily model time step. SWAT can be applied in watersheds up to several 1000s of km2, using a two-level disaggregation scheme. Preliminary sub-basin identifications are carried out based on topographic criteria, followed by further discretization using land use and soil type. The physical properties inside each sub-basin are then aggregated with no spatial significance.

In SWAT, a catchment is divided into multiple sub-catchments with hydrologic response units (HRUs) that consist of homogeneous land use, management, topographic, and soil characteristics (Abbaspour et al. 2007a). SWAT-CUP is a computer program that provided sensitivity analysis, calibration, validation, and uncertainty analysis of SWAT models (Abbaspour et al. 2007b).

The hydrology model is based in a water balance equation, comprising surface runoff, precipitation, evapotranspiration, infiltration and sub-surface runoff. Potential evapotranspiration is estimated using either the Priestley or Taylor (1972) or the Penman–Monteith (Monteith 1965) methods.

The soil profile is represented by up to 10 soil layers, a shallow aquifer and a deep aquifer. When the field capacity in one layer is exceeded, the water is routed to the lower soil layer. If this layer is already saturated, lateral flow occurs. From the bottom soil layer, percolation goes into the shallow and deep aquifers. Water reaching the deep aquifer is lost, but in turn flow from the shallow aquifer due to deep aquifer saturation that is added directly to the sub-basin channel.

Surface runoff is computed by the SCS curve number method (Arnold et al. 1990) and is therefore a non-linear function of precipitation and a retention coefficient. Once all hydrological processes are calculated for a homogeneous part of the sub-basin, the resulting flows are considered to contribute directly to the main channel. SWAT includes a routing module based on the Routing Outputs to Outlet model (ROTO; Arnold et al. 1990), which takes into account the connectivity of the river network.

Description the SUFI-2 algorithm

A common factor in almost all the published papers is the calibration/validation and uncertainty analysis of the models (Abbaspour et al. 2018). In SUFI-2, parameters uncertainty accounts for all sources of uncertainties. These sources include variables (e.g., rainfall), the conceptual model, model parameters and measured data. To evaluate such uncertainties, SUFI-2 offers two criterion factors, the P-factor and the R-factor. The P-factor indicates the percentage of measured data bracketed by the 95% prediction uncertainty (95PPU), whereas the R-factor calculates the average thickness of the 95PPUb and divided by the standard deviation of the measured data. Theoretically, the value of the P-factor ranges from 0 to 100%, while that of the R-factor ranges from 0 to infinity. A P-factor of 1 and R-factor of zero indicate a simulation that exactly complies with measured data.

Input data

Meteorological data with topography, soil and land-use maps are prepared to simulate model. Digital elevation model with resolution 90 meters is used to create topographic data (Fig. 2). Twenty-one hydrologic sub basins were identified in study area. SWAT model needs soil-type and land-use properties to simulate hydrological parameters within each sub-basin (Winchell et al. 2009).
Fig. 2

Digital elevation model (DEM) of Karaj basin

The volume of surface runoff is simulated using the curve number (CN) method. This method was created by Soil Conservation Service (USDA 1972) which calculates surface runoff as a function of soil type, slope, initial soil humidity and management practices(Neitsch et al. 2011). The evapotranspiration potential is determined using the Penman–Monteith equation and is corrected for the soil cover and simulated plant growth to get the real evapotranspiration rate (Neitsch et al. 2011; Monteith 1965).

The soil type classified into four classes by the FAO (1997) is presented in Fig. 3. The land-use map provided by SCWMRI (2002) is illustrated in Fig. 4.
Fig. 3

Distribution of soil classes within Karaj basin

Fig. 4

Land-use map of Karaj basin

The climatic data including daily precipitation, maximum and minimum temperature, relative humidity, solar radiation and wind speed were obtained from the Karaj meteorological station. The data used were for the period January 1, 1997–December 30, 2011.

Performance evaluation of the model

The performance of the model was evaluated by visual and statistical comparison of the measured and simulated data. The graphical technique provided an initial general overview (American Society of Civil Engineers 1993).Interpretation of the hydrograph first focused on the peak flows and then on the base flow. There are no existing standards describing the range of values of the statistical parameters that would show acceptable performance of the model (Loague and Green 1991). In this study, seven criteria including R2, NSE, PBIAS, RMSE, RSR, P-factor and R-factor are applied to analyze the model (Table 1). American Society of Civil Engineers (1993) has highlighted the Nash–Sutcliffe efficiency coefficient (NSE) (Nash and Sutcliffe 1970) among the various statistical parameters to evaluate hydrological models.
Table 1

Criteria for evaluating the performance of the hydrological model and their corresponding classifications

Statistical criterion

Ranges of values

Classification of performance

References

Coefficient of determination \(R^{2} = \left[ {\frac{{\sum\nolimits_{1}^{n} {\left( {O_{i} - \bar{O}} \right)} \left( {S_{i} - \bar{S}} \right)}}{{\left( {\sum\nolimits_{1}^{n} {(O_{i} - \bar{O})^{2} } } \right)^{{{\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-0pt} \!\lower0.7ex\hbox{$2$}}}} \left( {\sum\nolimits_{1}^{n} {(S_{i} - \bar{S})^{2} } } \right)^{{{{1} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-0pt} \!{2}}}} }}} \right]^{2}\)

\(0 \le R^{2} \le 0.5\)

Unsatisfactory

Steel and Torrie (1960)

\(0.5 \le R^{2} \le 1\)

Satisfactory

Nash–Sutcliffe efficiency \({\text{NSE}} = 1 - \frac{{\sum\nolimits_{1}^{n} {\left( {O_{i} + S_{i} } \right)^{2} } }}{{\sum\nolimits_{1}^{n} {\left( {O_{i} + \bar{O}} \right)^{2} } }}\)

0.75 < NSE ≤ 1.00

Very good

Boskidis et al. (2012) and Moriasi et al. (2007)

0.65 < NSE ≤ 0.75

Good

0.050 < NSE ≤ 0.65

Satisfactory

0.40 < NSE ≤ 0.50

Acceptable

NSE ≤ 0.40

Unsatisfactory

0.4 ≤ NSE ≤ 0.7

Acceptable

Percent bias \({\text{PBIAS}} = \frac{{\sum\nolimits_{i = 1}^{n} {\left( {O_{i} - S_{i} } \right)} \cdot 100}}{{\sum\nolimits_{1}^{n} {(O_{i} )} }}\)

PBIAS < ± 10

Very good

Moriasi et al. (2007)

± 10 ≤ PBIAS < ± 15

Good

± 15 ≤ PBIAS < ± 25

Satisfactory

PBIAS ≥ ± 25

Unsatisfactory

Root mean square error \({\text{RSME}} = \left[ {\frac{{\left( {\sum {S_{i} } - O_{i} } \right)^{2} }}{n}} \right]^{ - 15}\)

Value below half the standard deviation

Satisfactory

Singh et al. (2004)

Ratio of the RMSE to the standard deviation of the observation \({\text{RSR}} = \frac{{\text{RMSE}}{\text{STDEV}}} = \frac{{\left[ {\sqrt {\sum\nolimits_{n}^{1} {\left( {O_{i} - S_{i} } \right)^{2} } } } \right]}}{{\left[ {\sqrt {\sum\nolimits_{n}^{1} {\left( {O_{i} - \bar{O}} \right)^{2} } } } \right]}}\)

0.00 ≤ RSR ≤ 0.50

Very good

Moriasi et al. (2007)

0.50 < RSR ≤ 0.60

Good

0.60 < RSR ≤ 0.70

Satisfactory

RSR > 0.70

Unsatisfactory

P-factor

0 ≤ P-factor ≤ 1

1 = Good coincide

Yang et al. (2008a, b) and Narsimlu et al. (2015)

0 = Not coincide

\({\text{R-factor}} = \frac{{\frac{1}{n}\sum\nolimits_{{t_{i = 1} }}^{n} {\left( {\gamma_{{t_{i,97.5\% } }}^{M} - \gamma_{{t_{i,2.5\% } }}^{M} } \right)} }}{{\sigma_{\text{obs}} }}\)

0 ≤ P-factor ≤ ∞

0 = Good certainty

Yang et al. (2008a, b) and Narsimlu et al. (2015)

∞ = Uncertainty

i—time series of the measured and simulated pairs; n—number of pairs of the measured and simulated variables; Oi—observational data; Si—simulated data; \(\bar{O}\)—mean of the observational data

The P-factor (percentage of measured data bracketed by the 95% prediction boundary) is often named 95PPU (Percentage Prediction Uncertainty). The 95PPU is calculated at the 2.5% and 97.5% levels of the cumulative distribution of an output variable obtained through Latin hypercube sampling (Abbaspour 2011). The range of the P-factor varies from 0 to 1, with values close to 1 indicating good fitness between simulated and observed values (Yang et al. 2008a, b).

Another measure quantifying the strength of a calibration and uncertainty analysis is the R-factor, which is the average thickness of the 95PPU band divided by the standard deviation of the measured data. The calibrated parameter ranges can be generated with an acceptable value of the R-factor and P-factor.

Results and discussion

Sensitivity analysis of the model parameters

The sensitivity analysis procedure employed measurement data for the period January 1, 1997–December 30, 2011, to evaluate the fit between the measured and modeled time series data. This enabled the identification of the parameters that were influenced by the characteristics of the hydrographic basin, and those to which the model was most sensitive. Evaluation was then made of the way in which adjusting the value of a parameter affected the model output, in order to identify parameters that might improve the characteristics of the model (Veith and Ghebremichael 2009). Sensitive parameters were calculated using the SUFI-2 algorithm and SSE (sum of square errors) equation between the measured and simulated daily runoff data.

The parameters used for the flow were selected based on the experience in the study area. The initial simulation to determine the sensitivity of the model to different parameters was performed using default parameter values. The values were then varied within upper and lower limits established according to the characteristics of each parameter, using three methods. In the first procedure, the initial value of the parameter is modified by adding an increment. The second method consists of multiplying the initial value by a set amount. In the third method, the initial value is substituted by a different value (Van Griensven et al. 2007).

The sensitivity of the model to a parameter is determined using the percentage difference between the output values of the objective function for simulations performed immediately before and after changing the value of a parameter (Veith and Ghebremichael 2009). A higher t-stat value indicates greater sensitivity for a given parameter.

Thirty-five parameters selected for sensitivity analysis that eight parameters were more sensitive for simulation of the flow (Table 2). These parameters were CN2, ALPHA_BF, CH_K2, ESCO, CH_N2, REVAPMN, GW_REVAP and SOL_BD.
Table 2

Sensitive analysis of parameters in Sira station

Index

Parameter

Definition

T-stat

P value

Process

Ranks of sensitivity

1

CN2

SCS curve number for moisture condition IIa

13.136

0.00

Runoff

1

2

ALPHA_BF

Base-flow alpha factors (1 days−1)

12.472

0.00

Groundwater

2

3

CH_K2

Channel-effective hydraulic conductivity (mm h−1)

1.445

0.148

Channel

3

4

ESCO

Soil evaporation compensation factor

0.268

0.788

Soil

4

5

CH_N2

Manning’s n value for main channela

0.211

0.832

Channel

5

6

REVAPMN

Threshold depth of water in the shallow aquifer for ‘revap’ to occur (mm)

0.185

0.853

Groundwater

6

7

GW-REVAP

Snowmelt base temperature (°C)

0.872

0.161

Groundwater

7

8

SOL-BD

Soil bulk density (g/cm3)

0.130

0.896

Soil

8

aT-stat is then used to identify the relative significance of each parameters. The sensitives given above are estimates of the average changes in the objective function resulting from changes in each parameters, while all other parameters are changing

Calibration and validation of the parameters

This research passed through three stages which are as follows: (1) the setup (also known as warm-up) period from 1997 till the end of the year 1999 (3 years), (2) the calibration period which extended from the beginning of the year 2000 to the end of the year 2006 (7 years), and (3) a validation step covering the period 2007 to the end of the year 2011 (5 years). Usually in modeling, few initial years are selected for setup or warm-up of the model. In general, one-third and two-thirds of the original data sets are selected for model calibration and validation, respectively. Visual analysis of hydrographs shows overestimating of peak runoff with slightly wide uncertainty band (95PPU). Simulated runoff closed to observe one by selecting proper range of sensitive parameters.

The results of calibration period were obtained with four iterations and 500 runs during 2000–2006 (Fig. 5). The calibrated ranges of parameters were performed by model during validation period from 2007 to 2011 (Fig. 6). The results demonstrated that the performance of the calibrated and validated model was satisfactory, with NSE > 0.75, R2 > 0.5, PBIAS < ± 10, RMSE ≤ (SD/2), and 0.06 ≤ RSR ≤ 0.70. Main statistical criteria in calibration periods show R2, P-factor, R-factor and NSE are 0.58, 0.36, 0.36 and 0.57, respectively. This analysis shows 0.62, 0.63, 0.49 and 0.59 in validation period, respectively (Table 3). Coefficient of determination (R2) and Nash–Sutcliffe efficiency coefficient (NSE) show a reliable correlation between observed and simulated values in both calibration and validation periods. Statistical analyses are used to evaluate the simulation accuracy of the model performance. The observed and estimated mean monthly discharge at Sira hydrometry station in calibration and validation periods is plotted in Fig. 7a, b.
Fig. 5

The observed and estimated mean monthly discharge at Sira hydrometry station in calibration period (2000–2006)

Fig. 6

The observed and estimated mean monthly discharge at Sira hydrometry station in calibration period (2007–2011)

Table 3

Statistical analysis of runoff simulation in calibration and validation periods

 

Variable

R 2

NSE

PBIAS

RMSE

RSR

P-factor

R-factor

Calibration

FLOW_OUT

0.58

0.57

0.2

0.435

0.6

0.36

0.36

Validation

FLOW_OUT

0.62

0.59

8.4

0.058

0.70

0.63

0.49

Fig. 7

Scatter plot of river stream flow for a calibration period (2000–2006) and b validation period (2007–2011)

The results obtained for the statistical criteria demonstrated that the model was able to describe the hydrological processes in the watershed of the Karaj River.

Conclusion

Many urban and rural societies within Karaj River catchment are dependent on surface water resources. The long-term fluctuation in surface flow may have serious impacts on the daily lives of upstream residents as well as the reservoir and the capital residents in both terms of flooding and yield. SWAT model reproduced surface runoff in Karaj River reliably for both calibration and validation periods using the entire records of years 1997–2011. Seven main statistical criteria including R2, NSE, PBIAS, RMSE, RSR, P-factor and R-factor are used to evaluate the modeling results with the observed records of stream flow with minimum uncertainty. The use of sensitivity analysis enabled the identification of the most important parameters required to model the hydrological processes in the Karaj River catchment, hence reducing the quantity of model parameters that needed to be calibrated. In this study, eight sensitive parameters including CN2, ALPHA_BF, CH_K2, ESCO, CH_N2, REVAPMN, GW-REVAP and SOL-BD were used in a successful effort to decrease uncertainty.

Using the developed modeling and database platform, planners and decision makers will have access to a set of reliable tools to test any proposed scenarios of development within the different gauged or ungauged sub-catchments of the main basin and evaluate the impacts on surface water.

Notes

Acknowledgements

Authors wish to acknowledge Soil Conservation and Watershed Management Institute (SCWMRI) for making data base accessible for this study.

References

  1. Abbaspour KC (2011) SWAT-CUP4: SWAT calibration and uncertainty programs—a user manual. Swiss Federal Institute of Aquatic Science and Technology, EawagGoogle Scholar
  2. Abbaspour KC, Johnson C, Van Genuchten MT (2004) Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J 3:1340–1352CrossRefGoogle Scholar
  3. Abbaspour KC, Yang J, Maximov I, Siber R, Bogner K, Mieleitner J, Zobrist J, Srinivasan R (2007a) Modelling hydrology and water quality in the prealpine/alpine Thur watershed using SWAT. J Hydrol 333(2–4):413–430CrossRefGoogle Scholar
  4. Abbaspour KC, Vejdani M, Haghighat S (2007b) SWAT-CUP calibration and uncertainty programs for SWAT. In: Proc. intl. congress on modelling and simulation (MODSIM’2007), Melbourne, Australia, pp 1603–1609Google Scholar
  5. Abbaspour KC, Ashraf Vaghefi S, Srinivasan R (2018) A guideline for successful calibration and uncertainty analysis for soil and water assessment: a review of papers from the 2016 international SWAT conference. Water J 10:6.  https://doi.org/10.3390/w10010006 CrossRefGoogle Scholar
  6. American Society of Civil Engineers (1993) Criteria for evaluation of watershed models. J Irrig Drain Eng 119:429–442CrossRefGoogle Scholar
  7. Andersson L, Wilk J, Todd MC, Hughes DA, Earle A, Kniveton D, Layberry R, Savenije HHG (2006) Impact of climate change and development scenarios on flow patterns in the Okavango River. J Hydrol 331(1):43–57CrossRefGoogle Scholar
  8. Arnold JG, Fohrer N (2005) SWAT 2000: current capabilities and research opportunities in applied watershed modeling. Hydrol Process 19:563–572CrossRefGoogle Scholar
  9. Arnold JR, Williams AD, Nicks, Sammons NB (1990) A basin scale simulation model for soil and water resources management. Texas A & M University Press, TXGoogle Scholar
  10. Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) large area hydrologic modeling and assessment, part I: model development. J Am Water Resour Assoc 34(1):73–89CrossRefGoogle Scholar
  11. Beven K, Binley A (1992) the future of distributed models-model calibration and uncertainty prediction. Hydrol Process 6(3):279–298CrossRefGoogle Scholar
  12. Bilondi MP, Abbaspour KC, Ghahraman B (2013) Application of three different calibration-uncertainty analysis methods in a semi-distributed rainfall-runoff model application. Middle East J Sci Res 15(9):1255–1263Google Scholar
  13. Boskidis I, Gikas GD, Sylaios GK, Tsihruntzis VA (2012) Hydrologic and water quality modeling of lower Nestos river basin. Water Resour Manag 26:3023–3051CrossRefGoogle Scholar
  14. De Girolamo AM, Lo Porto A (2012) Land use scenario development as a tool for watershed management within the Rio Mannu Basin. Land Use Policy 29:691–701CrossRefGoogle Scholar
  15. Debele B, Srinivasan R, Parlange JY (2008) Coupling upland watershed and downstream water hydrodynamic and water quality models (SWAT and CE-QUAL-W2) for better water resources management in complex river basins. Environ Model Assess 13:135–153CrossRefGoogle Scholar
  16. Du J, Rui H, Zuo T, Li Q, Zheng D, Chen A, Xu Y, Xu CY (2013) Hydrological simulation by SWAT model with fixed and varied parameterization approaches under land use changes. Water Resour Manag 27:2823–2838CrossRefGoogle Scholar
  17. Fukunaga DC, Cecílio RA, Zanetti SS, Oliveira LT, Caiado MAC (2015) Application of the SWAT hydrologic model to a tropical watershed at Brazil. Catena 125:206–213CrossRefGoogle Scholar
  18. Hosseini M (2010) Effect of landuse changes on surface runoff and suspended sediment yield of Taleghan Catchment, Iran. Ph.D. Thesis, University Putra MalaysiaGoogle Scholar
  19. Hosseini M, Ashraf MA (2015) Application of the SWAT model for water components separation in Iran. Springer, TokyoCrossRefGoogle Scholar
  20. Hosseini M, Ghafouri M, Tabatabaei MR, Goodarzi M, AbdeKolahch A (2012) Effects of land use changes on water balance in Taleghan Catchment. J Agric Sci Technol 14:1159–1172Google Scholar
  21. Huang J, Zhou P, Zhou Z, Huang Y (2013) Assessing the influence of land use and land cover datasets with different points in time and levels of detail on watershed modeling in the north river watershed, China. Int J Environ Res Public Health 10:144–157CrossRefGoogle Scholar
  22. Huang SH, Krysanova V, Hattermann F (2015) Projections of climate change impacts on floods and droughts in Germany using an ensemble of climate change scenarios. Reg Environ Change 15(3):461–473CrossRefGoogle Scholar
  23. Krysanova V, Srinivasan R (2015) Assessment of climate and land use change impacts with SWAT. Reg Environ Change 15:431–434CrossRefGoogle Scholar
  24. Kuczera G, Parent E (1998) Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm. J Hydrol 211(1–4):69–85CrossRefGoogle Scholar
  25. Lin B, Chen X, Yao H, Chen Y, Liu M, Gao L, James A (2015) Analyses of landuse change impacts on catchment runoff using different time indicators based on SWAT model. Ecol Ind 58:55–63CrossRefGoogle Scholar
  26. Loague K, Green RE (1991) Statistical and graphical methods for evaluating solute transport models: overview and application. J Contam Hydrol 7:51–73CrossRefGoogle Scholar
  27. Monteith JL (1965) Evaporation and the environment. In: 19th Symposia of the society for experimental biology, vol 19, pp 205–235Google Scholar
  28. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Am Soc Agric Biol Eng 50:885–900Google Scholar
  29. Narsimlu B, Gosain AK, Chahar BR, Singh SK, Srivastava PK (2015) SWAT model calibration and uncertainty analysis for streamflow prediction in the Kunwari river basin, India, using sequential uncertainty fitting. Environ Processes 2:79–95CrossRefGoogle Scholar
  30. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual model. Part 1—a discussion of principles. J Hydrol 10:282–290CrossRefGoogle Scholar
  31. Neitsch SL, Arnold JG, Kiniry JR, Williams JR (2011) Soil and water assessment tool: theoretical documentation—version 2009. Texas Water Resources Institute Technical Report No. 406. Agricultural Research Service (USDA) and Texas Agricultural Experiment Station, Texas A&M University, TempleGoogle Scholar
  32. Niu J, Sivakumar B (2014) Study of runoff response to land use change in the east river basin in south China. Stoch Environ Res Risk Assess 28(4):857–865CrossRefGoogle Scholar
  33. Nyeko M (2015) Hydrologic modelling of data scarce basin with SWAT model capabilities and limitations. Water Resour Manag 29:81–94CrossRefGoogle Scholar
  34. SCWMRI (2002) Atlas land use map for Iran. Soil Conservation and Watershed Management InstituteGoogle Scholar
  35. Shen Z, Hong Q, Yu H, Niu J (2010) Parameter uncertainty analysis of non-point source pollution from different land use types. Sci Total Environ 408:1971–1978CrossRefGoogle Scholar
  36. Singh SK, Dutta S (2016) Effect of observational uncertainty on hydrological modeling. In: Proc. 56th New Zealand hydrological society’s the water and infrastructure and environmental conference, 28 Nov–2 Dec 2016, Queenstown, New ZealandGoogle Scholar
  37. Singh VP, Woolhiser DA (2002) Mathematical modeling of watershed hydrology. J Hydrol Eng 7:270–292CrossRefGoogle Scholar
  38. Singh J, Knapp HV, Demissie M (2004) Hydrologic modeling of the Iroquois river watershed using HSPF and SWAT. Illinois Department of Natural Resources and the Illinois State Geological Survey. Illinois State Water Survey Contract Report 2004-08Google Scholar
  39. Steel RGD, Torrie JH (1960) Principles and procedures of statistics. McGraw-Hill Book Company, New York, p 481Google Scholar
  40. USDA Soil Conservation Service (1972) National engineering handbook, section 4, hydrology. USDA Soil Conservation Service, Washington DCGoogle Scholar
  41. Van Griensven A, Meixner T (2006) Methods to quantify and identify the sources of uncertainty for river basin water quality models. Water Sci Technol 53(1):51–59CrossRefGoogle Scholar
  42. Van Griensven A, Meixner T, Grunwald S, Bishop T, Diluzio M, Srinivasan R (2007) A global sensitivity analysis tool for the parameters of multi-variable catchment models. J Hydrol 324:10–23CrossRefGoogle Scholar
  43. Veith TL, Ghebremichael LT (2009) How to: applying and interpreting the SWAT auto-calibration tools. In: Proceedings of the 5th international SWAT conference, Boulder, 5–7 August 2009, pp. 26–33Google Scholar
  44. Vilaysanea B, Takaraa K, Luob P, Akkharathc I, Duan W (2015) Hydrological stream flow modelling for calibration and uncertainty analysis using SWAT model in the Xedone river basin, Lao PDR. Procedia Environ Sci 28:380–390CrossRefGoogle Scholar
  45. Vrugt JA, TerBraak CJ, Clark MP, Hyman JM, Robinson BA (2008) Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov chain Monte Carlo simulation. Water Resour Res 44:W00B09.  https://doi.org/10.1029/2007wr006720 CrossRefGoogle Scholar
  46. Wang X, Melesse AM, Yang W (2006) Influences of potential evapotranspiration estimation methods on SWAT’s hydrologic simulation in a northwestern Minnesota watershed. Trans ASABE 49(6):1755–1771CrossRefGoogle Scholar
  47. Winchell M, Srinivasan R, Di Luzio JM (2009) ArcSWAT 2.3.4 interface for SWAT2005: user’s guide. Blackland Research Center: Texas Agricultural Experiment Station, Temple, p 465Google Scholar
  48. Wurbs RA (1998) Dissemination of generalized water resources models in the United States. Water Int 23:190–198CrossRefGoogle Scholar
  49. Yang J, Reichert P, Abbaspour KC, Xia J, Yang H (2008a) Comparing uncertainty analysis techniques for a SWAT application to Chaohe basin in China. J Hydrol 358(1–2):1–23CrossRefGoogle Scholar
  50. Yang J, Abbaspour KC, Reichert P, Yang H (2008b) Comparing uncertainty analysis techniques for a SWAT application to Chaohe basin in China. J Hydrol 358(1–2):1–23CrossRefGoogle Scholar
  51. Yang SK, Jung WY, Han WK, Chung IM (2012) Impact of land-use changes on stream runoff in Jeju Island, Korea. Afr J Agric Res 7(46):6097–6109CrossRefGoogle Scholar
  52. Zhang Y, Xia J, Shao Q, Zhai X (2011) Water quantity and quality simulation by improved SWAT in highly regulated Huai river basin of China. Stoch Environ Res Risk Assess 27(1):11–27CrossRefGoogle Scholar
  53. Zhang A, Zhang C, Fu G, Wang B, Bao Z, Zheng H (2012) Assessments of impacts of climate change and human activities on runoff with SWAT for the Huifa river basin, northeast China. Water Resour Manag 26:2199–2217CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Watershed and Rangeland, Faculty of Natural ResourcesSari Agricultural Sciences and Natural Resources UniversityMazandaranIran
  2. 2.Soil Conservation and Watershed Management Research Institute (SCWMRI)Agricultural Research, Education and Extension Organization (AREEO)TehranIran

Personalised recommendations