Integrating the Sacramento Conceptual Rainfall-Runoff Model and Reanalyzed Datasets for Runoff Simulation

Document Type : Research Paper

Authors

1 MSc student in Water Resources Engineering, Water engineering Dept., Imam Khomeini International University, Qazvin, Iran.

2 Assistant Professor in Water Engineering Department/ Imam Khomeini International University

Abstract

Accurate estimation of runoff plays an important role in water resources and hydrological studies. Due to simple structure and minimum data requirements, the conceptual hydrologic models are the best way to estimate runoff. The main objective of this study is to investigate the performance of Sacramento model in runoff simulation and determining the best reanalyzed evapotranspiration dataset for using in the model. In this study, four different datasets including HBV, ORCHIDEE, PCR-GLOBW, WATERGAP3 and W3RA are used in Sacramento model. Also, for estimation of basin-averaged rainfall time seri, the Thiessen method was used based on ground gage observations. Results indicate that using most of the reanalyzed datasets in Sacramento model lead to reliable outputs and the performance of model in simulation of daily stream flow is relatively high. However, the performance of model in the case of using W3RA and WATERGAP3 is better than the other data sources, and in both calibration and verification phases the Nash-Sutcliffe efficiency (NSE) is higher than 0.60 and 0.87, respectively. Moreover, findings show that the W3RA dataset is the best one for estimation of runoff volume, high flows (peak floods) and the time to peak flows. Overall, based on the outputs of this research, the reanalyzed datasets can be considered as an alternative or complementary in data-limited regions for water resources and hydrological studies. 

Keywords


Abbott M. B., Bathurst J. C., Cunge J. A., O’Connell P. E. and Rasmussen J. (1986) An introduction to the European Hydrological System – Systeme Hydrologique Europ ` een (SHE): 1. History and philosophy of a physically- ´ based, distributed modelling system. Journal of Hydrology, 87:45–59.
Beven K. J., Warren R. and Zaoui J. SHE. (1987) Towards a methodology for physically-based distributed forecasting in hydrology. Int. Assoc. Sci. Hydrol. Publ. No., 129:133–137.
Bhuiyan, M.A.E. (2018). Uncertainty of Global Precipitation Datasets and Its Propagation in Hydrological Simulations (Doctoral dissertation, University of Connecticut).
Devia, G.K., Ganasri, B.P. and Dwarakish, G.S. (2015). A review on hydrological models. Aquatic procedia, 4, 1001-1007.
d'Orgeval, T., Polcher, J., and de Rosnay, P. (2008). Sensitivity of the West African hydrological cycle in ORCHIDEE to infiltration processes, Hydrol. Earth Syst. Sci., 12, 1387-1401, doi:10.5194/hess-12-1387-2008.
Dutra, E. (2015). Report on the current state-of-the-art Water Resources Reanalysis, Earth2observe deliverable no. D. 5.1.
Eisner, S. (2016). Comprehensive evaluation of the WaterGAP3 model across climatic, physiographic, and anthropogenic gradients (Doctoral dissertation).
Grayson, R. and Blöschl, G. (2001). Spatial patterns in catchment hydrology: observations and modelling. CUP Archive.
Gupta, H.V., Kling, H., Yilmaz, K.K. and Martinez, G.F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of hydrology, 377(1-2), 80-91.
Gupta, H.V., Sorooshian, S. and Yapo, P.O. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. Journal of hydrologic engineering, 4(2), 135-143.
Haddeland, I., Clark, D.B., Franssen, W., Ludwig, F., Vo, F., Arnell, N.W., Bertrand, N., Best, S., Gerten, D. and Gomes, S. (2011). Multimodel estimate of the global terrestrial water balance: Setup and first results. Journal of Hydrometeorology, 12(5), 869-884.
Kratzert, F., Klotz, D., Brenner, C., Schulz, K. and Herrnegger, M. (2018). Rainfall–runoff modelling using long short-term memory (LSTM) networks. Hydrology and Earth System Sciences, 22(11), 6005-6022.
Krinner, G., Viovy, N., N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., F riedlingstein, P., Ciais, P., Stich, S., and Prentice, I. C. (2005). A dynamic global vegetation model for studies of the coupledatmosphere-biosphere system, Global Biogeochem. Cy, 19 (1), 10-25.
Leisenring, M. (2011). Implications of Hydrologic Data Assimilation in Improving Suspended Sediment Load Estimation in Lake Tahoe, California.
Li, H., Zhang, Y., Chiew, F.H.S., Xu, S. (2009). Predicting runoff in ungauged catchments by using Xinanjiang model with MODIS leaf area index. Journal of hydrology, 370 (1–4), 155–162.
Li, Y., Grimaldi, S., Pauwels, V.R. and Walker, J.P. (2018). Hydrologic model calibration using remotely sensed soil moisture and discharge measurements: The impact on predictions at gauged and ungauged locations. Journal of hydrology, 557, 897-909.
Lindström, G., Johansson, B., Persson, M., Gardelin, M. and Bergström, S. (1997). Development and test of the distributed HBV-96 hydrological model. Journal of hydrology, 201(1-4), 272-288.
Meng, X.Y., Wang, H., Cai, S.Y., Zhang, X.S., Leng, G.Y., Lei, X.H., Shi, C.X., Liu, S.Y. and Shang, Y. (2017). The China meteorological assimilation driving datasets for the SWAT model (CMADS) application in China: A case study in Heihe river basin.
Muthuwatta, L.P. et al. (2009). Calibration of a semi-distributed hydrological model using discharge and remote sensing data. In: Yilmaz, K.K. et al. (Eds.), New Approaches to Hydrologiocal Prediction in Data-Sparse Regions. IAHS, Hydrabad, 52–58.
Nash, J.E. and Sutcliffe, J.V. (1970). River flow forecasting through conceptual models’ part I—A discussion of principles. Journal of hydrology, 10(3), 282-290.
Ngo-Duc, T., Laval, K., Ramillien, G., Polcher, J., and Cazenave, A. (2007). Validation of the land water storage simulated by Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) with Gravity Recovery and Climate Experiment (GRACE) data, Water Resour. Res, 43, 4-20.
Perrin, C., Michel, C. and Andréassian, V. (2003). Improvement of a parsimonious model for streamflow simulation. Journal of hydrology, 279(1-4), 275-289.
Podger, G., 2004. Rainfall runoff library user guide. Cooperative Research Centre for Catchment Hydrology.
Qi, W., et al. (2016). Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations. Hydrology and Earth System Sciences, 20, 903–920.
Sood, A. and Smakhtin, V. (2015). Global hydrological models: a review. Hydrological Sciences Journal, 60(4), 549-565.
Van Beek, L.P.H., Bierkens, M.F.P. (2009). The Global Hydrological Model PCR-GLOBWB: Conceptualization, Parameterization and Verification. Department of Physical Geography, Faculty of Earth Sciences Utrecht University.