Sensitivity analysis of a GIS-based model: A case study of a large semi-arid catchment

Water resource and hydrologic modeling studies are intrinsically related to spatial processes of hydrologic cycle. Due to generally sparse data, and high rainfall variability, the accurate prediction of water availability in complex semi-arid catchment depends to a great extent on how well spatial input data describe realistically the relevant characteristics. The Geographic Information System (GIS) provides the framework within which spatially distributed data are collected and used to prepare model input files. Despite significant recent developments in distributed hydrologic modeling, the over-parameterization is usually a critical issue that can complicate calibration process. Sensitivity analysis methods reducing the number of parameters to be adjusted during calibration are important for simplifying the use of these models. The objective of this paper is to perform a sensitivity analysis for flow in a semi-arid catchment (1,491 km2), located in northwestern of Tunisia, using the Soil and Water Assessment Tool (SWAT) model. The simulation results revealed that among eight selected parameters, curve number (CN2), soil evaporation compensation factor (ESCO), soil available water capacity (SOL_AWC) and threshold depth of water in the shallow aquifer required for return flow (GWQMN) were found to be the most sensitive parameters. Calibration of hydrology, facilitated by the sensitivity analysis, was performed for the period 2001 through 2003. Results of calibration showed that the model accurately predict runoff and performed well with a monthly Nash Sutcliffe efficiency (NSE) of 0,78, a coefficient of determination (R2) of 0,85 and a percent of bias (PBIAS) equal to −13,22 %.

[1]  George H. Hargreaves,et al.  Reference Crop Evapotranspiration from Temperature , 1985 .

[2]  Jeffrey G. Arnold,et al.  EFFECT OF WATERSHED SUBDIVISION ON SWAT FLOW, SEDIMENT, AND NUTRIENT PREDICTIONS 1 , 2002 .

[3]  T. A. Costello,et al.  Effect of DEM data resolution on SWAT output uncertainty , 2005 .

[4]  A. Francosa,et al.  Sensitivity analysis of distributed environmental simulation models : understanding the model behaviour in hydrological studies at the catchment scale , 2015 .

[5]  G. R. Foster,et al.  WEPP: A new generation of erosion prediction technology , 1991 .

[6]  Raghavan Srinivasan,et al.  SWAT: Model Use, Calibration, and Validation , 2012 .

[7]  D. H. Pilgrim,et al.  Problems of rainfall-runoff modelling in arid and semiarid regions , 1988 .

[8]  Jeffrey G. Arnold,et al.  Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .

[9]  Peter A. Vanrolleghem,et al.  Sensitivity analysis for hydrology and pesticide supply towards the river in SWAT , 2005 .

[10]  A. Melesse,et al.  Hydrological analysis of the Upper Tiber River Basin, Central Italy: a watershed modelling approach , 2013 .

[11]  Soroosh Sorooshian,et al.  Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration , 1999 .

[12]  Steven T. Bednarz,et al.  LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART II: MODEL APPLICATION 1 , 1998 .

[13]  Indrajeet Chaubey,et al.  Sensitivity and identifiability of stream flow generation parameters of the SWAT model , 2010 .

[14]  H. Randy Gimblett,et al.  Integrating geographic information systems and agent-based modeling techniques for simulating social and ecological processes , 2001 .

[15]  Francisco Javier Elorza,et al.  Sensitivity analysis of distributed environmental simulation models: understanding the model behaviour in hydrological studies at the catchment scale , 2003, Reliab. Eng. Syst. Saf..

[16]  Mazdak Arabi,et al.  ROLE OF WATERSHED SUBDIVISION ON MODELING THE EFFECTIVENESS OF BEST MANAGEMENT PRACTICES WITH SWAT 1 , 2006 .

[17]  Richard H. Hawkins,et al.  Curve Number Hydrology : State of the Practice , 2008 .

[18]  A van Griensven,et al.  Sensitivity analysis and auto-calibration of an integral dynamic model for river water quality. , 2002, Water science and technology : a journal of the International Association on Water Pollution Research.

[19]  Vincent Chaplot,et al.  Impact of DEM mesh size and soil map scale on SWAT runoff, sediment, and NO3-N loads predictions , 2005 .

[20]  M. Vanclooster,et al.  Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France) , 2013 .

[21]  Application of Three Different Calibration-Uncertainty Analysis Methods in a Semi-Distributed Rainfall-Runoff Model Application , 2013 .

[22]  R. Young,et al.  AGNPS: A nonpoint-source pollution model for evaluating agricultural watersheds , 1989 .

[23]  Peter Krause,et al.  Quantifying the impact of land use changes on the water balance of large catchments using the J2000 model , 2002 .

[24]  Manel Mosbahi,et al.  INFLUENCE DES DONNEES D’ENTREE DU MODELE SWAT SUR LA QUANTIFICATION DES PERTES EN SOL DAN , 2008 .

[25]  Jiaping Wu,et al.  Effect of DEM resolution on SWAT outputs of runoff, sediment and nutrients , 2010 .

[26]  Jimmy R. Williams,et al.  Continuous-time water and sediment-routing model for large basins , 1995 .

[27]  Shivanand Balram,et al.  Integrating Geographic Information Systems and Agent-Based Modeling Techniques for Simulating Social and Ecological Processes , 2003, The Professional Geographer.

[28]  Jeffrey G. Arnold,et al.  The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions , 2007 .

[29]  David D. Bosch,et al.  Effect of spatial distribution of rainfall on temporal and spatial uncertainty of SWAT output. , 2009 .

[30]  K. Abbaspour,et al.  Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT , 2007 .

[31]  R. Srinivasan,et al.  A global sensitivity analysis tool for the parameters of multi-variable catchment models , 2006 .

[32]  Karim C. Abbaspour,et al.  Using monthly weather statistics to generate daily data in a SWAT model application to West Africa , 2007 .

[33]  Stephen R. Workman,et al.  SIMULATION OF DAILY AND MONTHLY STREAM DISCHARGE FROM SMALL WATERSHEDS USING THE SWAT MODEL , 2000 .

[34]  Raghavan Srinivasan,et al.  A modeling approach to evaluate the impacts of water quality management plans implemented in a watershed in Texas , 2006, Environ. Model. Softw..

[35]  Jeffrey G. Arnold,et al.  Soil and Water Assessment Tool Theoretical Documentation Version 2009 , 2011 .

[36]  V. Singh,et al.  Mathematical Modeling of Watershed Hydrology , 2002 .

[37]  John R. Williams,et al.  Sediment yield prediction based on watershed hydrology. , 1977 .

[38]  R. Morgan,et al.  A simple approach to soil loss prediction: a revised Morgan–Morgan–Finney model , 2001 .

[39]  Abdelhamid Fadil,et al.  Hydrologic Modeling of the Bouregreg Watershed (Morocco) Using GIS and SWAT Model , 2011, J. Geogr. Inf. Syst..

[40]  Ruimin Liu,et al.  Effect of Watershed Subdivision on SWAT Modeling with Consideration of Parameter Uncertainty , 2010 .

[41]  Nanée Chahinian,et al.  Flow and nutrient transport in intermittent rivers: a modelling case-study on the Vène River using SWAT 2005 , 2011 .

[42]  Dan B. Jaynes,et al.  Effect of the accuracy of spatial rainfall information on the modeling of water, sediment, and NO3–N loads at the watershed level , 2005 .

[43]  H. Gijzen.  Anaerobic digestion for sustainable development: a natural approach , 2002 .

[44]  S. Benabdallah,et al.  Modeling water quality to improve agricultural practices and land management in a tunisian catchment using the soil and water assessment tool. , 2014, Journal of environmental quality.

[45]  Indrajeet Chaubey,et al.  SENSITIVITY ANALYSIS, CALIBRATION, AND VALIDATIONS FOR A MULTISITE AND MULTIVARIABLE SWAT MODEL 1 , 2005 .

[46]  Sihem Benabdallah,et al.  Application of the SWAT model on the Medjerda river basin (Tunisia) , 2005 .