Calibration and Validation Parameter of Hydrologic Model HEC-HMS using Particle Swarm Optimization Algorithms – Single Objective

آب و خاک

Article 6, Volume 29, Issue 3, July 2015, Pages 615-626   

Document Type: Research Article

DOI: 10.22067/jsw.v0i0.37239

Authors

R. Garmeh^* ; Alireza Farid-hosseini; majid hashemi nia; A. Hojjati

Ferdowsi University of Mashhad

Abstract

Introduction: Planning and management of water resource and river basins needs use of conceptual hydrologic models which play a significant role in predicting basins response to different climatic and meteorological processes. Evaluating watershed response through mathematical hydrologic models requires finding a set of parameter values of the model which provides thebest fit between observed and estimated hydrographs in a procedure called calibration. Asmanual calibration is tedious, time consuming and requires personal experience, automaticcalibration methods make application of more significant CRR models which are based onusing a systematic search procedure to find good parameter sets in terms of at least oneobjective function.
Materials and Methods: Conceptual hydrologic models play a significant role inpredicting a basin’s response to different climatic and meteorological processes within natural systems. However, these models require a number of estimated parameters. Model calibration is the procedure of adjusting the parametervalues until the model predictions match the observed data. Manual calibration of high-fidelity hydrologic (simulation) models is tedious, time consuming and sometimesimpractical, especially when the number of parameters islarge. Moreover, the high degrees of nonlinearity involved in different hydrologic processes and non-uniqueness ofinverse-type calibration problems make it difficult to find asingle set of parameter values. In this research, the conceptual HEC-HMS model is integrated with the Particle Swarm Optimization (PSO) algorithm.The HEC-HMS model was developed as areplacement for HEC-1, which has long been considered as astandard model for hydrologic simulation. Most of thehydrologic models employed in HEC-HMS are event-basedmodels simulating a single storm requiring the specificationof all conditions at the beginning of the simulation. The soil moistureaccounting model in the HEC-HMS is the onlycontinuous model that simulates both wet and dry weatherbehavior.Programming of HEC –HMS has been done by MATLAB and techniques such as elite mutation and creating confusion have been used in order to strengthen the algorithm and improve the results. The event-based HEC-HMS model simulatesthe precipitation-runoff process for each set of parameter values generated by PSO. Turbulentand elitism with mutation are also employed to deal with PSO premature convergence. The integrated PSO-HMS model is tested on the Kardeh dam basin located in the Khorasan Razavi province.
Results and Discussion: Input parameters of hydrologic models are seldomknown with certainty. Therefore, they are not capable ofdescribing the exact hydrologic processes. Input data andstructural uncertainties related to scale and approximationsin system processes are different sources of uncertainty thatmake it difficult to model exact hydrologic phenomena.In automatic calibration, the parameter values dependon the objective function of the search or optimization algorithm.In characterizing a runoff hydrograph, threecharacteristics of time-to-peak, peak of discharge and totalrunoff volume are of the most importance. It is thereforeimportant that we simulate and observe hydrographs matchas much as possible in terms of those characteristics.
Calibration was carried out in single objective cases. Model calibration in single-objective approach with regard to the objective function in the event of NASH and RMSE were conducted separately.The results indicated that the capability of the model was calibrated to an acceptable level of events. Continuing calibration results were evaluated by four different criteria.Finally, to validate the model parameters with those obtained from the calibration, tests perfomed indicated poor results. Although, based on the calibration and verification of individual events one event remains, suggesting set is a possible parameter.
Conclusion: All events were evaluated by validations and the results show that the performance model is not desirable. The results emphasized the impossibility of obtaining unique parameters for a basin. This method of solution, because of non-single solutions of calibration, could be helpful as an inverse problem that could limit the number of candidates. The above analysis revealed the existence of differentparameter sets that can altogether simulate verificationevents quite well, which shows the non-uniqueness featureof the calibration problem under study. However, the methodologyhas benefited from that feature by finding newparameter intervals that should be fine-tuned further inorder to decrease input and model prediction uncertainties.The proposed methodology performed well in the automatedcalibration of an event-based hydrologic model;however, the authors are aware of a drawback of the presentedanalysis – this undertakingwas not a completely fair validationprocedure. It is because validation events represent possiblefuture scenarios and thus are not available at the time ofmodel calibration. Hence, an event being selected as a validationevent should not be used to receive any morefeedback for adjusting parameter values and ranges.However,this remark was not fully taken into consideration, mostlybecause of being seriously short of enough observed eventsin this calibration study. Therefore, the proposed methodology,although sound and useful, should be validated inother case studies with more observed flood events.

Keywords

HEC-HMS; Particle Swarm Optimization Algorithm; Validation

References

1- Chow V.T., Maidment D.R., and Mays L.W. 1988. Applied Hydrology, McGraw, Inc, New York, USA.

2- Duan Q., Sorooshian S., and Gupta H.V. 1994. Optimal use of the SCE-UA global optimization method for calibrating watershed models. Journal of Hydrology, 158: 265-284.

3- Eberhart R., Kennedy J. 1995. A New Optimizer Using Particle Swarm Theory Proc. Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, Piscataway, NJ: IEEE Service Center pp. 39-43.

4- Eckhardt K., Arnold J.G. 2001. Automatic Calibration of Distributed Catchment’s Model. Journal of Hydrology, 251:103-109.

5- Ensaniat N. 2012. Daily runoff simulation using the PSO algorithm in catchment model optimization. M.sc. Thesis. Azad University. Tehran. (in Persian with English abstract)

6- Gupta H.V., Sorooshian S., and Yapo P. 1999. Status of automatic calibration for hydrologic models comparison with multi-Level expert calibration. Journal of Hydrologic Engineering, 250: 135-143.

7- Jain S.K. 1993. Calibration of Conceptual Models for Rainfall-Runoff Simulation. HydrologicalSciences, 189: 431-441.

8- Kamali B., Mousavi J. 2010. Automatic Calibration of Hydrologic Event-Based Model Using PSO Meta-Heuristic Algorithm. Fifth National Congress on Civil Engineering. (in Persian with English abstract)

9- Kumar D.N., Reddy M.J. 2007. Multipurpose Reservoir Operation Using Particle Swarm Optimization. Journal of Water Resource Planning and Management, 133: 192-201.

10- Liu H., Abraham A. 2007. Calibration of Conceptual Models for Rainfall-Runoff Simulation. Journal of Innovative Computing and Applications, 139: 39-47.

11- Moussa R., Chahinian N. 2009. Comparison of Different Multi-Objective Calibration Criteria Using a Conceptual Rainfall-Runoff Model of Flood Events. Journal of hydrology and Earth System Sciences,194: 519-535.

12- Muleta M.K., Nicklow J.W. 2005. Sensitivity and Uncertainty Analysis Coupled with Automatic Calibration for a Distributed Watershed Model. Journal of Hydrology, 306: 127-145.

13- Parsopoulos K.E, Vrahatis M.N. 2002. Recent Approaches to Global Optimization Problems through Particle Swarm Optimization. Natural Computing, 1: 235-306.

14- Scharffenberger W.A., Fleming M.J. 2008. Hydrologic Modeling System HEC-HMS User’s Manual. USACE, 1-290.

15- Sorooshian S. (1983). Automatic Calibration of Conceptual Rainfall-Runoff Models; the Question of Parameter Observability and Uniqueness. Water Resource Research, 19: 260-268.

16- Timothy D.S., Charles S.M., Kyle E.K. 2000. Equations for Estimating Clark Unit Hydrograph Parameters for Small Rural Watersheds in Illinois. Water-Resources Investigations Report.